Optimal site selection for rainwater harvesting through a combined approach of GIS and MCDM

Optimal site selection for rainwater harvesting through a combined approach of GIS and MCDM | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Optimal site selection for rainwater harvesting through a combined approach of GIS and MCDM Hera Dutta, Seheba Yameen, Mushfiqul Alam, Pollen Chakma This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8449542/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Water scarcity is a critical issue in the northwestern part of Bangladesh, particularly across the Rangpur and the Rajshahi divisions. Rainwater harvesting (RWH) presents a low-cost and sustainable solution to address this challenge by increasing local water availability. This study determines potential sites for RWH implementation using Geographic Information System (GIS) integrated with the Analytic Hierarchy Process (AHP), a widely used Multi-Criteria Decision Making (MCDM) approach. Fourteen criteria, including nine bio-physical criteria and five socio-economic criteria, were chosen to evaluate RWH sites. The correlation of bio-physical and socio-economic criteria was analyzed through a single pairwise comparison matrix. This globally applicable methodology was executed in the northwestern part of Bangladesh, including Rangpur and Rajshahi divisions. A sensitivity analysis was conducted under four different weighting scenarios to demonstrate diversity in decision-making judgements. To select the optimal scenario, validation was performed through an accuracy assessment method, where kappa accuracy was calculated 60% for scenario 1, 63% for scenario 2, 70% for scenario 3, and 100% for scenario 4. Based on kappa accuracy, scenario 4 is the optimal scenario for future RWH initiatives in the study area, which shows 39%, 55%, and 6% of the total area as poor, moderate, and good RWH potential zones. The final output showed that Thakurgaon, Panchagarh, Kurigram, Lalmonirhat, Nilphamari districts, along with some parts of Pabna district, were the most suitable for harvesting rainwater. Rainwater harvesting Multi-criteria decision making (MCDM) Analytic hierarchy process (AHP) Geographic Information System (GIS) Remote sensing Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Figure 14 1. Introduction Freshwater scarcity is gradually becoming a global concern due to rapid population growth, unsustainable resource use, and climate change impacts. In Bangladesh, the northwestern divisions of Bangladesh (Rangpur and Rajshahi) face acute water shortages due to chronic drought, irregular rainfall, and limited water surface availability (Mahmoud et al. 2016 ). The topological feature of the northwestern zone makes this region’s climate arid, and as a result, drought occurs frequently (Das et al. 2023 ). With surface water decreasing, communities are becoming dependent on groundwater, resulting in aquifer depletion due to excessive agricultural extraction (Shahid and Hazarika 2010). Conventional water-reserving systems, such as ponds and canals, often fail to reserve water all year round. Improper maintenance and construction make these systems vulnerable to seasonal drying (Kränzlin 2000 ). Moreover, the deep tubewells' installation and maintenance costs impose financial burdens on smallholder farmers (Hoque and Hope 2020). To face these challenges, RWH has vast significance in the sustainable water management practice (Ward et al. 2012 ). RWH is defined as collecting and storing rainwater that falls on the rooftops, open surfaces, and other catchment areas, and later using it to overcome water scarcity. This system can be fitted in the Northwestern zone of Bangladesh due to its abundant but poorly distributed rainfall. To reduce the unfavourable effects of droughts, depending on its suitability, RWH should be practiced efficiently (Gavhane et al. 2023 ). In wet seasons, RWH enables communities to store water that can be used in dry periods, reducing pressure on aquifers and lessening dependence on costly irrigation systems (M. R. Hasan, Nuruzzaman, and Mamun 2019). It contributes to crop productivity, ensures early recovery of construction costs, and offers a low-cost, easy-to-maintain alternative with a high benefit-cost ratio (Goel and Kumar 2005; Amin 2005 ; Afsari et al. 2022). The selection of the RWH potential zone plays a vital role in increasing water availability in the semi-arid areas (Mbilinyi et al. 2007). Several studies in Bangladesh(Akter and Ahmed 2015; Rana and Moniruzzaman 2023; Tariqul Islam et al. 2017 ) have explored the suitability of RWH. But most of the studies have been done considering bio-physical criteria (e.g., slope, land use, rainfall), often overlooking the socio-economic criteria (e.g., distance from water bodies, population density). Again, most of the studies all over the world(Shadmehri Toosi et al. 2020; Al-Adamat et al. 2010; Al-Adamat et al. 2012 ; Al-shabeeb 2016 ) emphasized on simple binary approach to assess socio-economic parameters, instead of applying weightings to socio-economic criteria based on their degrees of importance. Moreover, very few studies showed the difference in RWH planning, which is influenced by the emphasis between socio-economic and biophysical priorities. Multi-criteria Decision-making process is a process used for making a transparent and structured decision for analysis by arranging different criteria in a suitable manner (Zlaugotne et al. 2020 ). There are several MCDM methods, each having its own calculation and technique for analysis. Weighted Overlay Analysis (WOA) and Fuzzy Logic Model (FLM) are some methods used in MCDM, where WOA uses AHP and multiple influencing parameters for decision making analysis (Hassan et al. 2025 ). Unlike many other methods, AHP enables comparisons on different parameters on a standardized scale and generates a consistency ratio, which helps to verify the accuracy of the evaluations (SAATY and KEARNS 1985). Table 1 Literature review about the effects of different criteria on RWH Potential Category Criteria Authors Effect on RWH Potential Correlation Biophysical Criteria Rainfall (Shadmehri Toosi et al. 2020) ; (Jha et al. 2014) Higher annual rainfall is more favorable as it provides more water for harvesting. Strong Slope (Rana and Moniruzzaman 2023) ; (Shadmehri Toosi et al. 2020) Low and medium slopes are more suitable for RWH structures as they facilitate water retention and reduce construction costs. Strong Soil Map (Jha et al. 2014) Clayey soils generate more runoff and are suitable for harvesting. Strong (Qays Hashim and Naba Sayl, 2020) Medium fine-grain soils are more suitable as they can retain a good amount of water. Groundwater Depth (AL-Shammari et al. 2021 ) A shallow GW table is preferable for artificial recharge. Weak Soil Depth (Mbilinyi et al. 2007) Deeper soils are more suitable for RWH as they can store more water. Weak Drainage network/density (Buraihi, Rashid, and Shariff 2015) Lower drainage density has higher suitability because it accumulates water. Moderate (Shadmehri Toosi et al. 2020) Higher drainage density helps runoff to flow continuously, which can be harvested immediately. Runoff potential map (Karimi and Zeinivand 2021) Higher runoff potential indicates more water available for collection. Strong (Balkhair and Ur Rahman 2021) Higher runoff depth is preferred over lower runoff depth. Landuse Landcover (Karimi and Zeinivand 2021) LULC is an important factor in surface runoff generation, considered as per the infiltration effect. Strong (AL-Shammari et al. 2021 ) Depression map (Tiwari, Goyal, and Sarkar 2018 ) A large depression near the drainage network is preferable. Moderate Elevation/DEM (Sayl, Mohammed, and Ahmed 2020 ) Depends on the flow accumulation stream network and slope. Strong Aspect (Darabi et al. 2021 ) The lower aspect is more suitable for RWH due to lower evaporation rates. Weak Temperature (Darabi et al. 2021 ) Higher temperatures within a specific range enhance the probability of RWH. Strong Stream order (Qays Hashim and Naba Sayl, 2020) Higher stream order is a sign of more tributaries, which is favorable for RWH. Moderate Geology (Mahmood et al. 2020 ) Depends on other criteria. Such as soil type, elevation, LULC, etc. Strong Evapotranspiration (Al-Khuzaie et al. 2020) Lower ET rates are more suitable for RWH. Strong Environmental factors (Nyirenda et al. 2021 ) Minimal environmental sensitivity is more suitable for RWH, posing less threat to ecosystems. Moderate Socio-economic Criteria Distance from agricultural land (Qays Hashim and Naba Sayl, 2020) Proximity to farms enhances RWH water use for irrigation. Moderate Distance from urban areas (Qays Hashim and Naba Sayl, 2020) Proximity to urban areas reduces transportation costs. Moderate Distance from roads / road network (Wu, Molina, and Hussain 2018) Proximity to roads improves accessibility and reduces transportation costs for water or materials. Moderate Distance from well / river / stream (Matomela, Li, and Ikhumhen 2020a) Proximity to smaller streams ensures effective water collection. Strong Distance from fault parameters (Qays Hashim and Naba Sayl, 2020) A greater distance from faults reduces the chance of RWH structure damage. Moderate Distance from drainage (Shadmehri Toosi et al. 2020) Distance from drainage networks ensures cleaner water. Moderate Population density (Darabi et al. 2021 ) Greater population density elevates the demand for water resources. Strong Household income level (Nyirenda et al. 2021 ) Higher-income households are more likely to maintain RWH technology. Weak The main objective of this study is to identify the suitable zones for the RWH system by creating a potential map with GIS by integrating thematic layers, using AHP for decision making in the northwest part (Rangpur and Rajshahi divisions) of Bangladesh, and introducing four scenarios to specify suitable sites more accurately. The novelty of this study lies in selecting fourteen criteria for analysis, where runoff, depression, evapotranspiration, groundwater table, distance from agricultural land, distance from urban area, and distance from streams were newly added to this study area. Also, most of the previous research analyzed bio-physical and socio-economic criteria separately (Shadmehri Toosi et al. 2020; Al-Adamat et al. 2010; Al-Adamat et al. 2012 ; Al-shabeeb 2016 ), which ignored the different degrees of importance of socio-economic criteria. By conducting a Boolean approach for socio-economic criteria, those studies neglected the influence of all other criteria for a location where the Boolean value of any socio-economic criteria was zero, indicating a weak correlation between all criteria. But in this study, both types of criteria were analyzed collectively through AHP, to make a strong correlation among criteria and give significance to the degrees of importance of socio-economic criteria. Moreover, considering four scenarios for analysis: (a) according to normalized weights from pairwise comparison matrix, (b) priority of the bio-physical criteria, (c) priority of the socio-economic criteria, and (d) equal importance to both bio-physical criteria and socio-economic criteria ensured the gradual effect of socio-economic criteria and enhanced the specification for optimal site selection. 2. Materials and methods All criteria were selected based on an assessment of the effect of the criteria analyzed in previous research and their relevance in this study (Table 1 ). Through a pairwise comparison matrix, all criteria were given weights and integrated in ArcGIS. Then, four scenarios were created by sensitivity analysis, and RWH potential map was generated for each scenario. Finally, using accuracy assessment, the optimal RWH potential map was selected. Figure 2 shows the workflow diagram of the methodology used in this study. 2.1. Study area The present study focuses on the northwestern zone of Bangladesh, specifically the divisions of Rangpur and Rajshahi (Fig. 1 ), which fall within latitudes of 23°43'N to 26°38'N and longitudes of 88°00'E to 89°26'E. This region is geographically bordered by India on the west and north, and is also internally divided by administrative boundaries. The Rangpur division has an area of 16185.01 km 2 , and the Rajshahi division has an area of 18174.4 km 2 (Bangladesh National Portal; Rukunujjaman 2016 ). 115 rivers in total, including 19 transboundary rivers, are located in the study area (Rana and Moniruzzaman 2023; BWDB,2005). The main major river, Padma, is situated in the southern part of the study area, which is bordered to the east by the Brahmaputra-Jamuna river system(Rana and Moniruzzaman 2023), and other minor seasonal streams are Atrai, Mohananda, Purnobhaba, etc (Tract et al. 2014 ; Ferozur et al. 2019 ). The study area consists of sixteen districts, with eight districts in each division(Rukunujjaman 2016 ) (Fig. 1 ). This region exhibits three topographical features: Floodplains, Barind tract, and the Himalayan Piedmont Zone. The area lies largely within the Barind Tract, which is a raised landform above the surrounding floodplains. It is made of older sediments that were deposited in the Pleistocene era through natural processes like river erosion and deposition. between 10°C and 20°C. Over time, a gradual cooling trend has been observed, with the average temperature decreasing by about 0.67°C each year (Nowreen et al. 2021 ). There is a gradual change in the slope in this area from west to east, varying from 0.70 m/km to 2.2 m/km (Ferozur et al. 2019 ). 80% of the annual rainfall occurs between May and September. Rajshahi typically receives 1542.1 mm to 2235.8 mm of average annual rainfall (Shamsuzzoha et al. 2014 ), and Rangpur receives approximately 2,268 mm of average annual rainfall (Ahmed et al. 2023 ). Geologically, this area is dominated by Quaternary alluvial formations, while the Barind soils consist mostly of compact Pleistocene clay, which is known for its low permeability and poor water retention. Seasonal droughts frequently occur during pre-monsoon and post-monsoon in the Barind Tract. This leads to severe water scarcity when the surface water body shrinks in the dry season (Rahman et al. 2017 ; Huq 2020 ; Ferozur Rahaman 2017 ; M. T. Hasan et al. 2022 ). The cities of these two divisions are based on two types, where Bogra, Dinajpur, and Rangpur are ranked as the most urbanized areas based on both street density and percentage of urban land for streets (Islam and Kamruzzaman 2015 ). The economy of the area is agriculture-based. 30% of the net cultivated area and 40% of the net irrigated area of Bangladesh are situated in this area; for example, one-third of the country’s total rice is cultivated in the northwestern region of Bangladesh (BADC). In many parts of the region, groundwater levels during the pre-monsoon season typically lie between 4 and 15 meters. However, in certain locations in the Barind area, the depth can reach around 34 meters. This makes it difficult for the farmers to get water for irrigation purposes (Ali et al. 2022 ). According to the Population and Housing Census Preliminary Report 2022, the population of Rangpur division was 17610955 persons, and for Rajshahi population was 20353116 persons. Though the population density of these two divisions is comparatively lower than other dense divisions of Bangladesh (Dhaka and Chattogram), due to drought, lowering of the groundwater table, and other phenomena, water scarcity is increasing day by day in this region. Considering the region’s challenging climate, groundwater conditions, irrigation purposes, and other demands, RWH can be a solution to meet the water needs of the local communities. 2.2. Data collection and pre-processing For the RWH Potential map, fourteen variables were considered as influencing factors, which were classified as biophysical criteria and socio-economic criteria. The biophysical criteria included - Annual average Rainfall, Slope, Drainage Density, Soil Map, Runoff, Landuse Landcover (LULC), Evapotranspiration (ET), Topographic Depression, and Groundwater table depth. Socio-economic criteria consisted of Population density, Proximity to Urban areas, streams, roads, and agricultural lands. These data were collected from multiple sources, based on their relevance to the RWH system (Table 2 ). The annual average Rainfall data were collected from CHIPRSv. 03 for the years 2014 to 2024 with a spatial resolution of 5 kilometres. DEM (Digital Elevation Model)-based data, i.e., the Slope, Depression, and Distance from stream, were derived from SRTM 1 Arc-second DEM via the USGS Earth Explorer, which provides information at 30-meter spatial resolution globally. Pre-classified LULC datasets were derived from Sentinel-2 imagery with a spatial resolution of 10 meters, accessed via ESRI’s ArcGIS Living Atlas, which was further used in generating the map of distance from urban areas and agricultural lands. For runoff discharge calculation, Curve number data were obtained from the Global Hydrologic Curve Number dataset; Evapotranspiration data were obtained from MODIS MOD16A2 Version 6 using Google Earth Engine (GEE). The direct shapefile of the streams for the drainage density map was assessed from HydroSHEDS. The soil data was sourced from the FAO Soil portal. The population density data, shapefile of roads, and information were collected from WorldPop, DIVA-GIS, respectively. The groundwater table depth was collected in CSV format from the Bangladesh Water Development Board (BWDB). The DEM-based data, including Rainfall, Evapotranspiration, Landuse Landcover, and Curve Number data, were downloaded in TIFF format. Table 2 Details about data collection and tools used in preparing the layers Criteria Year Format Source ArcGIS Tools Rainfall 2014–2024 TIFF CHIRPS v3.0 IDW Slope 2014 TIFF USGS Earth Explorer (SRTM 1 ARC SECOND GLOBAL) Slope Soil 2007 Shapefile FAO Soils Portal Drainage Density N/A Shapefile HydroSHEDS Line Density tool Runoff 2019 TIFF GCN250 IDW Landuse Landcover 2024 TIFF Esri Sentinel-2 Evapotranspiration 2001–2024 TIFF NASA MODIS IDW Depression 2014 TIFF USGS Earth Explorer (SRTM 1 ARC SECOND GLOBAL) Fill Groundwater Table N/A CSV BWDB IDW Population Density 2020 TIFF WorldPop IDW Distance from Agricultural land 2024 TIFF Esri Sentinel-2 Euclidean Distance Distance from Urban Area 2024 TIFF Esri Sentinel-2 Euclidean Distance Distance from Road N/A Shapefile DIVA-GIS Euclidean Distance Distance from Stream 2014 Shapefile USGS Earth Explorer (SRTM 1 ARC SECOND GLOBAL) Euclidean Distance All the spatial layers were clipped to the study area extent, reprojected to the Universal Transverse Mercator (UTM) 45N coordinate system with WGS 1984 Datum, the raster layers were resampled to a common resolution of 30 meters, and no data values were set to zero during the analysis. 2.3. Multi-criteria decision making approach In this study, a well-known MCDM approach, called the Analytic Hierarchy Process (AHP) (Saaty 1980 ), was used to create correlations among the criteria. In any problem, judgment depends on certain criteria, which do not have the same impact. AHP principles are used to turn the relative importance of criteria into numbers. Different weights are given to certain criteria based on their relative importance, which can lead to a pairwise comparison matrix. Then, the normalized weight of all criteria is calculated, which shows the actual significance of each criterion in the decision-making process (Saaty 1980 ). All criteria (bio-physical and socio-economic) were selected as per the literature review and opinions from the experts with backgrounds in Civil Engineering and Water Resources Engineering. As the beginning step, an n × n pairwise comparison matrix (where n is the number of criteria) was created. The weights of the criteria and their features were assigned using a 1 to 9 scale (equal importance to extreme importance)(Saaty 1980 ) by the experts considering the geological and social conditions of the study area (Table 3 ). M n×n = C 1 C 2 \(\:\dots\:\) C n C 1 \(\:\frac{{w}_{1}}{{w}_{1}}\) \(\:\frac{{w}_{1}}{{w}_{2}}\) \(\:\dots\:\) \(\:\frac{{w}_{1}}{{w}_{n}}\) C 2 \(\:\frac{{w}_{2}}{{w}_{1}}\) \(\:\frac{{w}_{2}}{{w}_{2}}\) \(\:\dots\:\) \(\:\frac{{w}_{2}}{{w}_{n}}\) \(\:\:\:⋮\) \(\:\:\:⋮\) \(\:\:\:⋮\) \(\:\:\:⋮\) C n \(\:\frac{{w}_{n}}{{w}_{1}}\) \(\:\frac{{w}_{n}}{{w}_{2}}\) \(\:\dots\:\) \(\:\frac{{w}_{n}}{{w}_{n}}\) Here, M n×n is the pairwise comparison matrix (where n = 14 for our study). C 1 , C 2, …, C n denote criteria, and w 1 , w 2 , …, w n denote the relative weight of each criterion. Then the sum of each column of the matrix was calculated, and each entry of the matrix was divided by its column sum to derive the relative importance of the criteria, compared across columns: $$\:{\stackrel{-}{M}}_{jk\:}=\frac{{M}_{jk}}{{\sum\:}_{i=1}^{n}{\stackrel{-}{M}}_{ik}}$$ 1 Finally, the normalized weight vector was determined by averaging the relative importance of the criteria across rows (Table 4 ): $$\:{N}_{j}\:=\:\frac{{\sum\:}_{i=1}^{n}{\stackrel{-}{M}}_{ji}}{n}$$ 2 The criteria normalized weight shows rainfall as the most impactful criterion and distance from urban area as the least impactful criterion (Table 4 ). To validate the normalized weight generated by AHP, the consistency ratio (CR) should be determined. The acceptable value of CR is equal to or less than 10%. A value of CR greater than 10% indicates that the pairwise matrix needs to be revised (Saaty 1980 ). $$\:CR\:=\frac{CI}{RI}$$ 3 where CI is the consistency index and RI is the random consistency. RI is a constant value for different numbers of criteria. In this study, RI = 1.58 (for fourteen criteria). Table 3 Pairwise comparison matrix used in AHP to show the relative significance of all criteria Criteria RF SL ST DD RO LL ET DP GT PD DA DU DR DS Rainfall 1 3 4 4 3 3 7 7 5 6 4 8 6 4 Slope 1/3 1 3 3 1 1 6 6 4 5 3 7 5 3 Soil Type 1/4 1/3 1 1 1/3 1/3 5 5 3 4 1 5 4 1 Drainage Density 1/4 1/3 1 1 1/3 1/3 5 5 3 4 1 5 4 1 Runoff 1/3 1 3 3 1 1 6 6 4 5 3 7 5 3 Landuse Landcover 1/3 1 3 3 1 1 6 6 4 5 3 7 5 3 Evapotranspiration 1/7 1/6 1/5 1/5 1/6 1/6 1 1 1/4 1/2 1/7 2 1/2 1/7 Depression 1/7 1/6 1/5 1/5 1/6 1/6 1 1 1/4 1/2 1/7 2 1/2 1/7 Groundwater Table 1/5 1/4 1/3 1/3 1/4 1/4 4 4 1 2 1/2 4 2 ½ Population Density 1/6 1/5 1/4 1/4 1/5 1/5 2 2 1/2 1 1/4 3 1 ¼ Dist. from Agricultural Land 1/4 1/3 1 1 1/3 1/3 7 7 2 4 1 5 4 1 Dist. from Urban Area 1/8 1/7 1/5 1/5 1/7 1/7 1/2 1/2 1/4 1/3 1/5 1 1/3 1/9 Dist. from Road 1/6 1/5 1/4 1/4 1/5 1/5 2 2 1/2 1 1/4 3 1 1/5 Dist. from Stream 1/4 1/3 1 1 1/3 1/3 7 7 2 4 1 9 5 1 CI is determined from a formula: $$\:CI\:=\frac{{\lambda\:}_{max}\:-\:n}{n-1}$$ 4 where \(\:{\lambda\:}_{\text{m}\text{a}\text{x}}\) is the principal eigenvector, determined by the eigenvector technique. The CR value determined from this study was 5.6% (Table 4 ), which is acceptable. Table 4 Criteria normalized weight calculation following Table 3 Criteria RF SL ST DD RO LL ET DP GT PD DA DU DR DS CNW* RF 0.25 0.35 0.21 0.21 0.35 0.35 0.11 0.11 0.16 0.14 0.21 0.11 0.13 0.21 0.213 SL 0.08 0.11 0.16 0.16 0.11 0.11 0.10 0.10 0.13 0.11 0.16 0.10 0.11 0.16 0.126 ST 0.06 0.03 0.05 0.05 0.03 0.03 0.08 0.08 0.10 0.09 0.05 0.07 0.09 0.05 0.066 DD 0.06 0.03 0.05 0.05 0.03 0.03 0.08 0.08 0.10 0.09 0.05 0.07 0.09 0.05 0.066 RO 0.08 0.11 0.16 0.16 0.11 0.11 0.10 0.10 0.13 0.11 0.16 0.10 0.11 0.16 0.126 LL 0.08 0.11 0.16 0.16 0.11 0.11 0.10 0.10 0.13 0.11 0.16 0.10 0.11 0.16 0.126 ET 0.03 0.02 0.01 0.01 0.02 0.02 0.01 0.01 0.00 0.01 0.00 0.02 0.01 0.00 0.016 DP 0.03 0.02 0.01 0.01 0.02 0.02 0.01 0.01 0.00 0.01 0.00 0.02 0.01 0.00 0.016 GT 0.05 0.03 0.01 0.01 0.03 0.03 0.06 0.06 0.03 0.04 0.02 0.05 0.04 0.02 0.039 PD 0.04 0.02 0.01 0.01 0.02 0.02 0.03 0.03 0.01 0.02 0.01 0.04 0.02 0.01 0.024 DA 0.06 0.03 0.05 0.05 0.03 0.03 0.11 0.11 0.06 0.09 0.05 0.07 0.09 0.05 0.069 DU 0.03 0.01 0.01 0.01 0.01 0.01 0.00 0.00 0.00 0.00 0.01 0.01 0.00 0.00 0.013 DR 0.04 0.02 0.01 0.01 0.02 0.02 0.03 0.03 0.01 0.02 0.01 0.04 0.02 0.01 0.024 DS 0.06 0.03 0.05 0.05 0.03 0.03 0.11 0.11 0.06 0.09 0.05 0.13 0.11 0.05 0.075 *CNW = Criteria Normalized Weight \(\:{\lambda\:}_{\text{m}\text{a}\text{x}}\) = 15.162 & CR = 0.056 2.4. Layer selection The criteria for RWH systems were selected through a deep literature review, expert consultation, geological conditions of the study area, and data availability. From the previous literature review, it was observed that most of the studies focused on the biophysical criteria, mainly rainfall, slope, landuse landcover, and drainage density. Since 2000, the socio-economic criteria have been integrated into the studies of selecting sites for RWH systems (Ammar et al. 2016 ; Shadmehri Toosi et al. 2020). In this study, an additional criterion, evapotranspiration, was used to make the analysis more precise and correct (Al-Khuzaie et al. 2020). The criteria are further described below. 2.4.1. Bio-physical criteria 2.4.1.1. Rainfall The most important and influential criterion recognized for the RWH system is rainfall(Ammar et al., 2016 ; Jha et al., 2014; Rana & Moniruzzaman, 2023; Shadmehri Toosi et al., 2020). The more the rainfall is, the higher the potential for RWH systems in an area, since it impacts the sub-surface water storage and potential runoff(Ammar et al., 2016 ; Rana & Moniruzzaman, 2023). The inverse distance weight (IDW) spatial analyst tool was used to create the rainfall map in the studied area. The highest and lowest rainfall were observed to be in the range of 1455 mm and 3553.5 mm, respectively. This rainfall dataset was compared with data from the Bangladesh Meteorological Department (BMD). The rainfall range was further classified into 5 classes (Fig. 3 a). Rainfall less than 1500 mm was weighted as 3, and rainfall greater than 2500 mm was weighted as 9, as the higher the rainfall, the higher the potential for RWH systems. Meanwhile, the ranges,1501–1800 mm, 1801–2100 mm, and 2101-2500mm were given weights of 4, 5, and 7, respectively. 2.4.1.2. Slope The slope of the terrain is one of the most important criteria for selecting sites for RWH, as the runoff of rainwater and infiltration of soil depend on it directly (Shadmehri Toosi et al. 2020). The slope map was generated in ArcGIS 10.8 using the slope analyst tool on the Digital Elevation Model (DEM) of the study area. The mosaic tool was used to combine all the raster files before extracting the study area. The slope map was classified into 0–1, 1.1-3, 3.1-5, 5.1–10, and greater than 10 (Fig. 3 b). According to the suitability based on the water accumulation and expert opinion, these classes were weighted as 9, 8, 7, 5, and 1, respectively, where it showed that lower slopes were considered to be suitable for selecting RWH sites. 2.4.1.3. Soil type Infiltration characteristics of a soil affect the potential run-off of an area. More impermeability of a soil increases the capacity to store water and enhances the retention of rainwater (Shadmehri Toosi et al. 2020; Jha et al. 2014). The areas with the most clayey soils are recommended for the selection of RWH zones, as clayey soils are the most impermeable and can retain rainwater for a longer time. The study area was classified into five types of soil classes, including clay, loam, sandy clay loam, sandy loam, and water (Fig. 3 c), which were weighted as 9, 5, 7, 3, and 1, respectively. The clay-type soil was assigned a higher weight as this soil type is highly suitable for the RWH system. 2.4.1.4. Drainage Density Storing rainwater involves the drainage density of an area, as lower drainage density indicates a lower amount of surface runoff loss and higher storage capacity of water. The lower the density the suitability of RWH reduces and vice versa(Jha et al., 2014). The mosaic tool was used to combine raster files before extracting the study area, and by using the Line Density Spatial Analyst Tool in ArcGIS 10.8, the final drainage density map was generated. The drainage density map was generated using the Line Density Spatial Analyst Tool in ArcGIS 10.8. The range of the drainage density was classified into 3 classes (Fig. 3 d). Drainage density less than 0.2 km/sq. km was assigned a weight of 8 as it indicates a more suitable condition for RWH; density range of 0.21 to 4 km/sq. km was given a weight of 5, indicating moderate suitability, and drainage density greater than 0.4 km/sq. km was assigned a weight of 3, which shows that the area is not suitable for RWH. 2.4.1.5. Runoff Runoff indicates the excess rainfall that does not infiltrate into the soil and is available for surface flow. In this study, the higher the rate of runoff, the more weight was assigned to it. The runoff map was generated using the mostly used SCS-CN method (Subramanya 2008 ; Ramakrishnan and Kusuma 2009 ). The runoff depth was calculated using the formula: $$\:Q=\frac{{\left(P-\lambda\:S\right)}^{2}}{P+\left(1-\lambda\:\right)S}$$ 5 $$\:S=\frac{25400}{CN}-254$$ 6 where, Q = Runoff Depth (mm) P = Amount of rainfall (mm) S = Potential maximum retention λ = Initial abstraction ratio CN = Curve Number The value of λ was taken as the standard value of 0.2 (Subramanya 2008 ; Victor Ponce and Hawkins 1996). Both the curve number map and the rainfall map were resampled to 30m spatial resolution and projected to the Universal Transverse Mercator (UTM) 45N coordinate system with WGS 1984 Datum. Further, the runoff rate was obtained by, $$\:Q\left(discharge\right)=\frac{Q\left(depth\right)*Area}{Time}$$ 7 The highest amount of runoff was about 2.8 m 3 /sec. The runoff map was then reclassified into 0–1, 1.1-2, and 2< (Fig. 4 a). Each of them was assigned a weight of 3, 6, and 7, respectively, which shows that the higher the runoff rate the suitability of RWH increases. 2.4.1.6. Landuse Landcover Different types of landuse have different impacts on selecting RWH zones. The landuse landcover map was directly obtained from Esri Sentinel in TIFF format in 10m resolution, which was verified using Google Earth Pro. It was further resampled to a 30m spatial resolution and reclassified (Fig. 4 b). Landuse where the chances of infiltration were high, like trees and flooded vegetation, was assigned a lower and moderate weight, respectively (Shadmehri Toosi et al. 2020). It is nearly impossible to build a structure in water for the RWH system, and the (a) (b) suitability for constructing RWH structures in built-up areas is less favorable, as it affects the daily human activities and existing infrastructures. So, these landuse were assigned a lower weight in terms of other classes. The best suitable sites are selected near cultivated land and bare ground, which were weighted as 8 and 9, respectively. 2.4.1.7. Evapotranspiration As evapotranspiration reduces the volume of water stored, it influences RWH fashionably (Shadmehri Toosi et al. 2020; AL-Shammari et al. 2021 ). A smaller amount of evapotranspiration increases the suitability of RWH. The evapotranspiration map was reclassified into five classes (Fig. 4 c). The ranges were < 300 mm, 301–500 mm, 501–700 mm, 701–900 mm, and 900 < mm, which were given weights as 9, 7, 5, 3, and 1, respectively. 2.4.1.8. Depression Areas with a higher volume of depressions are highly suitable for harvesting rainwater as they catch a sufficient amount of runoff volume and reduce evaporation losses (Tiwari et al. 2018 ). Moreover, areas with depression are more suitable for installing ponds and recharge pits. The depression map was generated using the fill tool in ArcGIS 10.8 and reclassified into 3 classes, which ranged from 0–2 m, 2.1-5 m, and 5 m< (Fig. 4 d), and their assigned weight were 1, 3, and 5, respectively. 2.4.1.9. Groundwater Table A deeper groundwater table enhances the facility of rainwater to infiltrate through soil, which increases the suitability of constructing any artificial recharge-based RWH structure (AL-Shammari et al. 2021 ). The IDW spatial analyst tool was used to determine the continuous groundwater depth in the study area. Groundwater table depth (Fig. 5 a) less than 5 meters was given a weight of 1, and greater than 15 meters was assigned as 9. The intermediate ranges 5.1 -8, 8.1–12, and 12.1–15 meters were given a weight of 2, 4, and 6, respectively. 2.4.2. Socio-economic Criteria 2.4.2.1. Population Density Areas with higher population density are more suitable for harvesting rainwater, as the water can be utilized properly and meet the demand of the people. (Darabi et al., 2021 ).The IDW spatial analyst tool was used to generate the population density map of the study area. The population density was further classified into five classes: less than 900, 901–920, 921–950, 951–1000, and greater than 1000 persons/sq. km (Fig. 5 b). The assigned weights to these classes were 5,6,7,8, and 9, respectively. 2.4.2.2. Distance from agricultural land For the irrigation of lands, the RWH harvesting sites should be near cultivated lands (Qays Hashim and Naba Sayl 2020). The distances (Fig. 6 a) less than 500 meters are weighted as 9, and greater than 2000 meters are weighted as 1, as the proximity of the water source ensures efficient use of water for crop production. 2.4.2.3. Distance from urban area Due to insufficient space and difficulties in construction, and the risk of flooding, areas located at a greater distance from urban areas are more suitable for RWH (Qays Hashim and Naba Sayl 2020). The distances were classified into five groups: <1000 m, 1001–2000 m, 2001–3000 m, 3000–4000 m, and 4000m < (Fig. 6 b). Since the suitability increases as the distance increases from an urban area, distances greater than 4000m were assigned a weight of 7, and other classifications were weighted according to their significance. 2.4.2.4. Distance from road A greater distance from the roads can increase the transportation cost while constructing any RWH structures (Rana and Moniruzzaman 2023; Qays Hashim and Naba Sayl 2020). Considering the following facts, the distances from roads were classified into five classes: <1500 meters, 1501–3000 meters, 3001–4500 meters, 4501–6000 meters, and 6000 < meters (Fig. 6 c). The ranges were assigned a weight of 2, 9, 7, 5, and 3. The map was generated using the Euclidean Distance tool in ArcGIS 10.8. 2.4.2.5. Distance from stream The map of the distance from streams was generated using the Euclidean distance tool in ArcGIS. As the distance increases, the availability of water reduces (Matomela et al. 2020). Therefore, areas that are distant from a stream are considered more suitable for RWH sites as the sites have limited access to natural water resources. The distances were reclassified into five classes (Fig. 6 d). Distance from stream less than 2000 meters were assigned a weight of 1, and distances greater than 8000 meters were assigned as 9. The intermediate ranges were given weight based on their relative importance. 2.5. Sensitivity analysis Sensitivity analysis recognizes the importance of any factor or parameter by observing the change in the result after adjusting the value of some parameters. This analysis finds the sensitive parameters for an approach (Oh et al. 2011 ; Balkhair and Ur Rahman 2021; Wu et al. 2018). In this study, a sensitivity analysis was conducted on the criteria type (bio-physical and socio-economic). From the pairwise comparison matrix, three different scenarios were created by changing the normalized weight of both criteria types to a certain ratio, named as ‘case 1 ’ (scenario 2), ‘case 2’ (scenario 3), and ‘case 3’ (scenario 4) (Table 5 ). On the other hand, the scenario that was created from the correlation of criteria through the pairwise comparison matrix was named ‘base AHP’ (scenario 1). In case 1 , the assigned normalized weight of bio-physical criteria was three times larger compared to socio-economic criteria. In case 2, the assigned normalized weight of socio-economic criteria was three times larger than bio-physical criteria. In case 3, the assigned normalized weight of both bio-physical and socio-economic criteria was equal. The consistency ratio of all cases was within the acceptable range. Figure 7 represents the changing pattern of criteria normalized weight based on different scenario. Table 5 Criteria normalized weight for all different scenario based on sensitivity analysis Criteria Type Criteria Base AHP Case 1 Case 2 Case 3 Bio-physical Rainfall 0.213 0.201 0.067 0.134 Slope 0.126 0.119 0.040 0.079 Soil Type 0.066 0.062 0.021 0.042 Drainage Density 0.066 0.062 0.021 0.042 Runoff 0.126 0.119 0.040 0.079 Landuse Landcover 0.126 0.119 0.040 0.079 Evapotranspiration 0.017 0.016 0.005 0.011 Depression 0.016 0.015 0.005 0.010 Groundwater Table 0.039 0.037 0.012 0.025 Socio-economic Population Density 0.024 0.029 0.088 0.059 Distance from Agricultural Land 0.069 0.084 0.252 0.168 Distance from Urban Area 0.013 0.016 0.048 0.032 Distance from Road 0.024 0.029 0.088 0.059 Distance from Stream 0.075 0.091 0.274 0.183 2.6. RWH potential zone mapping To get the final output map, a GIS-based approach, combined with the MCDM process, was taken. The Weighted Linear Combination (WLC) method was used in ArcGIS to make a combination of all thematic layers according to their impact for the identification of the RWH potential zone. WLC is a simple method for the assessment of RWH potential zone and has greater acceptability in previous studies (Al-Adamat et al. 2010; Shadmehri Toosi et al. 2020; Akter and Ahmed 2015). Every criterion has different kinds of features. So, the relative weight of each feature of each criterion was given according to their importance using a scale of 1 to 9, and the normalized weight was calculated, which is shown in the Table 6 . All weights were decided through a literature review and expert opinion (Rana and Moniruzzaman 2023; Jha et al. 2014; Shadmehri Toosi et al. 2020; Saha et al. 2024 ; Al-Adamat, Diabat, and Shatnawi 2010; Matomela, Li, and Ikhumhen 2020). Then, the thematic layers were processed in ArcGIS based on the normalized weight of their features. Thus, a simple mathematical equation was used in the ArcGIS environment to create the RWH potential zone map for this study: RWHPI = (RF) c (RF) f + (SL) c (SL) f + (ST) c (ST) f + (DD) c (DD) f + (RO) c (RO) f + (LL) c (LL) f + (ET) c (ET) f + (DP) c (DP) f + (GT) c (GT) f + (PD) c (PD) f + (DA) c (DA) f + (DU) c (DU) f + (DR) c (DR) f + (DS) c (DS) f (8) where RWHPI represents the RWH Potential Index in this study area, the criteria normalized weight is denoted by subscript c , and the normalized weight of a feature of each criterion is denoted by subscript f (Shadmehri Toosi et al. 2020) and: RF = Rainfall criteria SL = Slope criteria ST = Soil Type criteria DD = Drainage Density criteria RO = Runoff criteria LL = Landuse Landcover criteria ET = Evapotranspiration criteria DP = Depression criteria GT = Groundwater Table criteria PD = Population Density criteria DA = Distance from Agricultural Land criteria DU = Distance from Urban Area criteria DR = Distance from Road criteria DS = Distance from Stream criteria Table 6 Assigned feature weights and justification for criteria used in RWH potential mapping Criteria Feature Class Weight Normalized Weight Reason Rainfall < 1500 3 0.11 More rainfall, more suitability (more rainfall provides more water for harvesting) 1501–1800 4 0.14 1801–2100 5 0.18 2101–2500 7 0.25 2500< 9 0.32 Slope < 1 9 0.30 Less slope, more suitability (based on water accumulation) 1.1-3 8 0.27 3.1-5 7 0.23 5.1–10 5 0.17 10< 1 0.03 Soil Type Clay 9 0.36 More impermeable layer, more suitability (it can store more water) Loam 5 0.20 Sandy Clay Loam 7 0.28 Sandy Loam 3 0.12 Water 1 0.04 Drainage Density < 0.2 8 0.50 Less drainage density, more suitability (better water accumulation, less runoff loss) 0.21–0.4 5 0.31 0.4< 3 0.19 Runoff < 1 3 0.19 More runoff, more suitability (more water available for collection) 1.1-2 6 0.37 2< 7 0.44 Landuse Landcover Water 1 0.03 The different feature shows different suitability (based on structure creation) Tree 2 0.06 Flooded Vegetation 5 0.16 Cultivated Land 8 0.25 Built-up Area 3 0.09 Bare Ground 9 0.28 Wetland 4 0.13 Evapotranspiration < 300 9 0.36 Less evapotranspiration, more suitability (because it reduces the amount of water) 301–500 7 0.28 501–700 5 0.20 701–900 3 0.12 900< 1 0.04 Depression < 2 1 0.11 More depression, more suitability (for natural storage) 2.1-5 3 0.33 5< 5 0.56 Groundwater Table < 5 1 0.05 More depth, more suitability (on the basis of groundwater recharge demand) 5.1-8 2 0.09 8.1–12 4 0.18 12.1–15 6 0.27 15< 9 0.41 Population Density < 900 5 0.14 More density, more suitability (for demand purpose) 901–920 6 0.17 921–950 7 0.20 951–1000 8 0.23 1000< 9 0.26 Distance from Agricultural land < 500 9 0.36 Less distance, more suitability (for irrigation purposes) 501–1000 7 0.28 1001–1500 5 0.20 1501–2000 3 0.12 2000< 1 0.04 Distance from Urban Area < 1000 1 0.06 More distance, more suitability (less demand of urban people, difficult to make a structure in a built-up area) 1001–2000 2 0.11 2001–3000 3 0.16 3001–4000 5 0.28 4000< 7 0.39 Distance from Road < 1500 2 0.08 Average distance, more suitability (for transportation) 1501–3000 9 0.35 3001–4500 7 0.27 4501–6000 5 0.19 6000< 3 0.11 Distance from Stream < 2000 1 0.04 More distance, more suitability (already has water sources) 2001–4000 3 0.12 4001–6000 5 0.20 6001–8000 7 0.28 8000< 9 0.36 The final output map was created based on RWHPI, which is a dimensionless indicator to classify the map into many layers according to the suitability condition of RWH potentiality of the study area. For this study area, the map was classified into three classes (poor, moderate, good) according to RWHPI based on the Jenks Natural Break method. This method is useful for getting an optimum arrangement of values to convert into the suitability classes (Jenks, 1967 ). 3. Results and Discussion 3.1. RWH potential zone map All fourteen criteria (nine bio-physical and five socio-economic criteria) were integrated according to their reclassification to overlay the RWH potential zone map using ArcGIS. 3.1.1. Rainfall map The map shows that rainfall is increasing gradually towards the northern portion of the study area (Fig. 3 a). The rainfall amount of the Rangpur division is much higher than that of the Rajshahi division. The highest value of rainfall was found at the northernmost part of the Rangpur division. 1501–1800 mm rainfall covers 35% of the study area, which is the highest amount of area among all feature classes of rainfall. Lowest rainfall (less than 1500 mm) occurs in 2%, and the highest rainfall (greater than 2500 mm) occurs in 19% of the study area (Table 7 ). 3.1.2. Slope map This study area has mostly gentle slope (Fig. 3 b), which increases the chances of harvesting rainwater. The maximum portion (60%) of the study area has a 1.1 to 3 degree slope. The lowest amount of slope (less than 1 degree) covers 25% of the study area, and the steepest slope (greater than 10 degrees) covers only 1% of the study area (Table 7 ). 3.1.3. Soil type map The study area is composed of four types of soil (Fig. 3 c). Most of the soil is loam, which covers 69% of the study area. Other types are clay, sandy clay loam, and sandy loam, which occupy respectively 11%, 4% and 14% of the study area. The remaining area (2%) is occupied by water (Table 7 ). 3.1.4. Drainage density map 75% of the total area has a drainage density of 0.21–0.40 km/km 2 , which indicates the study area has moderate drainage density. Least drainage density (less than 0.2 km/km 2 ) covers 5% and the highest drainage density (more than 0.4 km/km 2 ) covers 20% of the study area (Table 7 ). The different ranges of drainage density are uniformly distributed through the Rangpur and the Rajshahi divisions (Fig. 3 d). Table 7 Total area and area percentages of every feature of all criteria used in RWH potential mapping Criteria Feature Class Area (sq. km.) Area (%) Rainfall < 1500 471 2.00 1501–1800 12130 35.00 1801–2100 10020 29.00 2101–2500 5250 15.00 2500< 6679 19.00 Slope < 1 8724 25.00 1.1-3 20816 60.00 3.1-5 4294 12.00 5.1–10 615 2.00 10< 22 1.00 Soil Type Clay 3788 11.00 Loam 23738 69.00 Sandy Clay Loam 1483 4.00 Sandy Loam 4835 14.00 Water 687 2.00 Drainage Density < 0.2 1428 5.00 0.21–0.4 26007 75.00 0.4< 7080 20.00 Runoff < 1 3141 9.00 1.1-2 26674 77.00 2< 4735 14.00 Landuse Landcover Water 1674 5.00 Tree 2134 6.30 Flooded Vegetation 14 0.04 Cultivated Land 21981 63.66 Built-up Area 7250 21.00 Bare Ground 883 2.50 Wetland 591 1.50 Evapotranspiration < 300 501 1.50 301–500 8727 25.198 501–700 23813 69.00 701–900 1485 4.30 900< 0.5 0.002 Depression < 2 31847 92.00 2.1-5 2419 7.00 5< 254 1.00 Groundwater Table < 5 5451 16.00 5.1-8 19014 55.00 8.1–12 4992 14.00 12.1–15 2695 8.00 15< 2398 7.00 Population Density < 900 6576 19.00 901–920 36 0.10 921–950 103 0.30 951–1000 5046 14.60 1000< 22767 66.00 Distance from Agricultural land < 500 27341 79.00 501–1000 5971 17.00 1001–1500 502 1.40 1501–2000 538 1.60 2000< 196 1.00 Distance from Urban Area < 1000 31447 91.00 1001–2000 2434 7.00 2001–3000 473 1.44 3001–4000 170 0.50 4000< 23 0.06 Distance from Road < 1500 7968 23.00 1501–3000 4946 14.00 3001–4500 5816 17.00 4501–6000 3496 10.00 6000< 12322 36.00 Distance from Stream < 2000 27758 80.35 2001–4000 6362 18.50 4001–6000 375 1.00 6001–8000 28 0.10 8000< 13 0.05 3.1.5. Runoff map Though the runoff amount is slightly higher in the Rangpur division (Fig. 4 a), mainly moderate runoff was generated throughout the whole study area. 9%, 77% and 14% of the study area show less than 1 m 3 /sec, 1.1-2 m 3 /sec, and more than 2 m 3 /sec runoff (Table 7 ). 3.1.6. Landuse landcover map This study area is mainly agricultural (Fig. 4 b), having 63.66% of the total area, which is the highest among the seven types of landuse landcover. It indicates a great rainwater demand for farming activities. 21% area contains built-up area, 6.3% area contains forest and trees, 5% area contains water bodies, and a very small amount of areas (2.5%, 1.5% and 0.04%) have bare ground, wetland, and flooded vegetation, respectively (Table 7 ). 3.1.7. Evapotranspiration map A balanced scenario of evapotranspiration can be seen from the map (Fig. 4 c). The maximum (69%) area shows an evapotranspiration rate of 501–700 mm/year. The lowest (less than 300 mm/year) and the highest (greater than 900 mm/year) evapotranspiration rate covers very little area, which are respectively 1.5% and 0.002% of the total area (Table 7 ). 3.1.8. Depression map The depression map shows a very small depression in the soil surface (Fig. 4 d). About 92% of the study area has less than 2 m of depression (Table 7 ), which indicates that people can use depression as a natural storage for smaller purposes. Very little area of the Rajshahi division has slightly higher depression, whereas the Rangpur division has uniformly lower depression. 3.1.9. Groundwater table map The map presents a significantly deeper groundwater table in the Rajshahi division. On the other hand, the Rangpur division contains most of the shallow groundwater table (Fig. 5 a). Overall, the maximum part (55%) of the whole study area has groundwater table of 5.1-8 m. The shallowest and the deepest groundwater table (less than 5 m and greater than 15 m) covers 16% and 7% of the whole area, respectively (Table 7 ). 3.1.10. Population density map It can be seen from the map that this study area is overpopulated (Fig. 5 b). The maximum portion (66%) of the study area occupies the highest population density of greater than 1000 person/km 2 . Less than 900 person/km 2 population density can be seen only in 19% of the study area (Table 7 ). 3.1.11. Distance from agricultural land map As this study area is dominated by agricultural lands (Fig. 4 b), maximum areas are near any farm. The map illustrates, maximum areas (79%) are within 500 m of any agricultural land, and only 1% of the total area has a distance of 2000 m or more from any farm (Table 7 ). 3.1.12. Distance from urban area map After cultivated lands, this study area is dominated by urban areas (Fig. 4 b). Urban areas are more in the Rangpur division than in the Rajshahi division. Therefore, most of the total area (91%) is within the nearest distance (less than 1000 m) from any built-up areas (Table 7 ). 3.1.13. Distance from road map Distance from road map creates a relation between the transportation system with the RWH potential. A roughly balanced condition can be seen in the map according to the area percentage of different ranges (Fig. 6 c). Most of the area (36%) has a distance of greater than 6000 m from the road network. Again, less than 1500 m distance is covered by 23% of the study area. Each moderate distance ranges also have from 10% to 17% of the total area (Table 7 ). 3.1.14. Distance from stream map This map (Fig. 6 d) represents the distance between each place and the closest stream. About 80.35% of the study area is within 2000 m of any stream. The highest distance (greater than 8000 m) is shown by only 0.05% of the total area (Table 7 ). 3.1.15. RWH potential map All criteria layers were integrated and combined to calculate the RWH potential index (RWHPI). Total four RWH potential zone map were created from different normalized weight scenario after sensitivity analysis: (a) ‘base AHP’ (scenario from normalized weight using pairwise comparison matrix), (b) ‘case 1 ’ (bio-physical criteria dominating scenario), (c) ‘case 2’ (socio-economic criteria dominating scenario) and (d) ‘case 3’ (equal importance to both bio-physical and socio-economic criteria). The map displays RWHPI values from 0.12 to 0.33, which were classified into three suitability classes: (a) ‘poor’ (RWHPI ranges from 0.12 to 0.21), (b) ‘moderate’ (RWHPI ranges from 0.22 to 0.24) and (c) ‘good’ (RWHPI ranges from 0.25 to 0.33). In this classification, ‘poor’ indicates unsuitable RWH zone, ‘moderate’ and ‘good’ indicate the possibility of RWH, where ‘good’ presents the most suitable RWH zone. 3.1.15.1. Base AHP In this scenario, an area of 5784 km 2 (17%) is covered by the ‘poor’ RWH potential zone. 18263 km 2 , which is the majority of the total area (54%) are in the ‘moderate’ RWH potential zone, distributed uniformly all over the study area. An area of 9930 km 2 (29%) is covered by the ‘good’ RWH potential zone. The northernmost region dominates with good RWH potential, and almost no poor RWH potential area in that region (Fig. 8 a). 3.1.15.2. Case 1 Case 1 shows ‘moderate’ RWH potential zone dominates (56%) the output map, covering an area of 19027 km 2 . The area percentage of ‘poor’ and ‘good’ RWH potential zones is close to each other, occupying an area of 6795 km 2 (20%) and 8155 km 2 (24%) of the study area, respectively. This map (Fig. 8 b) displays an almost identical pattern to the output map of the scenario ‘base AHP’ (Fig. 8 a). 3.1.15.3. Case 2 ‘Poor’ RWH potential zone occupies a large portion (66%) of the map of case 2 (Fig. 8 c), which is an area of 22425 km 2 . ‘Moderate’ RWH potential zone covers an area of 10873 km 2 (32%) of the study area. Only 2% (679 km 2 ) of the study area is covered by ‘good’ RWH potential zone. 3.1.15.4. Case 3 In this scenario (Fig. 8 d), ‘poor’, ‘moderate’, and ‘good’ RWH potential zone occupies an area of 13251 km 2 (39%), 18687 km 2 (55%), and 2039 km 2 (6%) of the total area, respectively. This map shows better RWH potential than case 2 (Fig. 8 c) in the northernmost part. 3.1.16. Sensitivity analysis through area comparison From the calculation of the area percentage of the four scenarios (Fig. 9 a), a significant change can be seen in each class (‘poor’, ‘moderate’, and ‘good’) of each map. The general correlation (from ‘base AHP’ scenario) of both types of criteria shows bio-physical criteria domination over socio-economic criteria, which is almost as same as case 1 . For this reason, the area percentages of each class of ‘base AHP’ and ‘case 1 ’ are almost similar. But in case 2 and case 3, a significant normalized weight change of both types of criteria occurred, which created an impact on the area percentage of the suitability class. In case 2 and case 3, the area of ‘poor’ RWH potential zone has increased by 49% and 22% respectively from the ‘base AHP’ scenario, 46% and 19% respectively from case 1 . On the other hand, in case 2 and case 3, the area of ‘good’ RWH potential zone has decreased by 27% and 23%, respectively, from the ‘base AHP’ scenario, 22% and 18% respectively, from case 1 . Though from the ‘base AHP’ scenario and case 1 , the area percentage of ‘moderate’ RWH potential zone has reduced almost half in case 2, remains almost the same in case 3. 3.2. Validation When a new analytical approach is going to be implemented for publication or regular use, it is important to validate the output to verify the process or ensure the quality of the result. Without validation, the whole approach loses its acceptability (Chung and Fabbri 2003; Peters et al. 2007). Sometimes, an error can occur that might remain undetected while the study was conducted. For this reason, a proper method of validation is needed (Peters et al. 2007). In this study, the validation process was executed through accuracy assessment method. 3.2.1. Accuracy assessment To determine the optimal RWH potential map out of every scenario, an accuracy assessment was conducted. Accuracy assessment is necessary to verify the output from remotely sensed data to increase its reliability (Congalton 1991 ). Accuracy assessment is executed by choosing some random points in the generated map, comparing the point result from the map with the ground truth data (Aronoff 1985 ). From the comparison of the map and ground truth data, an error or confusion matrix is built (Congalton 1991 ; Story and Congalton 1986). Finally, 4 accuracy indicators: (a) producer’s accuracy, (b) user’s accuracy, (c) overall accuracy, and (d) kappa accuracy are calculated to come to a final decision (Wu et al. 2018; Congalton 1991 ). For accuracy assessment, the known ground truth value is the most important thing, which needs to be collected accurately. In this study, RWH potential maps were generated using a new analytical approach according to the relative importance of fourteen criteria, which were calculated from a pairwise comparison. So, it is difficult to define ground truth values for a new RWH potential map, which was generated through a complex analysis of many criteria. But to validate the output maps and determine the optimal RWH potential map from four scenarios, some obvious known ground truth values were considered, as per opinions from experts. Firstly, 150 random accuracy assessment points for each scenario were created using ArcGIS (Fig. 10 ). Then, the points which were located exactly at the river, road, or dense urban area were classified as ‘poor’ RWH potential zone for ground truth value because it is impossible to create an RWH structure in those types of locations. The points which were located at existing RWH structures like ponds, pans, and nala bunds were classified as ‘good’ RWH potential zone for ground truth value. All ground truth points were evaluated using Google Earth Pro satellite imagery. Among 150 accuracy assessment points, only these points with obviously known values from the ground were considered for generating a confusion matrix (Table 8 ), based on ‘poor’ and ‘good’ classes, which led to a limited but proper accuracy assessment with valid reasoning. 3.2.2. Base AHP Out of 150 points, a total of 59 points satisfied the condition of being a known ground truth value in this scenario. After generating the confusion matrix, the resulting producer’s accuracy was 81%, the user’s accuracy was 83%, the overall accuracy was 80% and the kappa accuracy was 60% (Table 9 ). 3.2.3. Case 1 Of the 150 random points created for this scenario, only 46 points met the requirement of functioning as reference ground truth data. The accuracy parameter value was 81% for producer’s accuracy, 86% for user’s accuracy, 83% for overall accuracy, and 63% for kappa accuracy (Table 9 ). Table 8 Generated confusion matrix for all scenario from the random point values of RWH potential map and ground truth values Base AHP Class Poor Good Total Poor 22 1 23 Good 11 25 36 Total 33 26 59 Case 1 Class Poor Good Total Poor 13 1 14 Good 7 25 32 Total 20 26 46 Case 2 Class Poor Good Total Poor 51 3 54 Good 0 4 4 Total 51 7 58 Case 3 Class Poor Good Total Poor 54 0 54 Good 0 2 2 Total 54 2 56 3.2.4. Case 2 A total of 58 points were considered as ground truth values in case 2. In this scenario, the resulting producer’s accuracy was 79%, the user’s accuracy was 97%, the overall accuracy was 95% and the kappa accuracy was 70% (Table 9 ). 3.2.5. Case 3 A total of 56 points among the random 150 accuracy assessment points were verified as the known ground truth value. In case 3, all 4 accuracy parameters showed 100% accuracy (Table 9 ), which was determined from the confusion matrix (Table 8 ). Thus, this validation process indicates that case 3 is the optimal RWH potential scenario in this study. Figure 11 shows the comparison between all accuracy parameter for all scenario. Table 9 Main accuracy indicator of confusion matrix for all scenario Scenario Accuracy Poor Good All Base AHP Producer's 67% 96% 81% User's 96% 69% 83% Overall 80% Kappa 60% Case 1 Producer's 65% 96% 81% User's 93% 78% 86% Overall 83% Kappa 63% Case 2 Producer's 100% 80% 90% User's 99% 100% 99% Overall 99% Kappa 88% Case 3 Producer's 100% 100% 100% User's 100% 100% 100% Overall 100% Kappa 100% 3.3. Discussion 3.3.1. Scenario Analysis This study focuses on mapping suitable zones for RWH with geospatial techniques and MCDM approaches, incorporating both bio-physical and socio-economic criteria. A sensitive analysis was done after the weighted overlay analysis, which demonstrated that changing the weight of social-economic criteria caused a gradual change in the percentage of area of good, moderate, and poor zones. This analysis validates that the socio-economic criteria should have their own weightage or degree of influence, much like bio-physical factors. Some previous studies incorporated the analysis of socio-economic factors by the Boolean threshold method (Shadmehri Toosi et al. 2020; Al-Adamat et al. 2010; Al-Adamat et al. 2012 ; Al-shabeeb 2016 ). But in this study, the socio-economic criteria were given their weight through AHP to correlate with bio-physical criteria perfectly. To understand how each of the criteria influences the suitability for RWH, four scenarios were analyzed. The base AHP scenario map focused on the AHP-derived weightage from literature review and expert opinion, case 1 focused on the biophysical criteria, the socio-economic criteria dominated in case 2, and in case 3, both biophysical and socio-economic criteria were given equal importance. The changes in weightage (Fig. 7 ) had a huge impact on the suitability map. Scenario of the base AHP and case 1 showed almost relatively similar outcomes, which showed a larger extent of good and moderate suitability of RWH. On the other hand, since case 2 emphasized the socio-economic criteria, there was a drastic change in the percentage of area in good, moderate, and poor suitable zones compared to base AHP scenario and case 1 . Case 3, which assigned equal importance to both biophysical and socio-economic criteria, showed a more balanced and favorable result (Fig. 8 ). Further, an accuracy assessment was done, and validation was done through analyzing the kappa coefficient(Al-Khuzaie et al. 2020) Here, the result of the validation showed that case 3, with a kappa coefficient of 1.0, is the most suitable scenario for RWH site selection, outperforming the other three scenarios, where base AHP scenario, case 1 , and case 2 had a kappa coefficient of 0.60, 0.63, and 0.70, respectively (Fig. 11 ). The RWH map of case 3 showed that Rangpur division, having 11% of suitable sites for RWH, is more promising than Rajshahi division, which had only 2% of suitable zones for RWH in the north-west part of Bangladesh (Fig. 9 ). From the map, it was identified that the most promising suitable zones for RWH were in the Thakurgaon, Panchagarh, Kurigram, Lalmonirhat, and Nilphamari districts, along with some parts in Pabna district. In the previous study of (Rana and Moniruzzaman 2023), in the same study area, it was found that the districts Shirajganj, Kurigram, Gaibandha, Rangpur, Thakurgaon, Dinajpur, Panchagarh, and Nilphamari were mostly suitable for RWH structures. On the other hand, Pabna, Natore, Rajshahi, Nawabganj, Naogaon, Joypurhat, and Bogra are less ideal for RWH structures. There are certain differences in both studies; this may be due to the criteria selected for analysis and the time range of collected data that were used. 3.3.2. Criteria analysis The study explored the correlation-based regression analysis (Fig. 12 , Fig. 13 , Fig. 14 ) between different criteria and the RWH Potential Index (RWHPI) to understand the impact of each criterion(Jacob Cohen et al. 2003 ) in the final optimal output (RWH potential map of case 3). The R 2 value from the regression indicated the influence of bio-physical and socio-economic factors on the RWH potential map (García and Caselles 1991; Al-Khuzaie et al. 2020). Among all the bio-physical criteria, rainfall (Fig. 12 a) and runoff (Fig. 12 d) showed the strongest correlation, R 2 of 0.209 and 0.2118, respectively, indicating that the suitability of RWH increases with higher rainfall and runoff. In socio-economic criteria, the distance from agricultural land (Fig. 13 c) dominated the most by having the strongest correlation with RWHPI (R 2 = 0.302), with a negative slope indicating that the proximity to agricultural land increases the suitability for RWH. The other factors showed a moderate to low relation with RWHPI. Regarding landuse landcover (Fig. 14 b), cultivated land showed the highest mean RWHPI (0.220). Areas with sandy loam soil have a higher mean RWHPI in the output map (0.224) (Fig. 14 a). Although the study hypothesized that lower evaporation rate, an increase in population density, and lower proximity to urban areas increase the suitability of RWH, the regression analysis showed a contradictory result in these three criteria. These may have occurred because other criteria dominated the suitability score that the effect of these three factors were reduced and showed an opposite result. All the analysis demonstrate that in the northwest part of Bangladesh, most of the suitable site for RWH is located cultivation is high. Based on this insight, it can be concluded that on the northwest side of Bangladesh, the main focus for harvesting rainwater should be irrigation support for agriculture. Some structures, like ponds and pans, and percolation tanks, are suggested for RWH for irrigation purposes (Shadmehri Toosi et al. 2020). 4. Conclusion For arid and semi-arid areas, RWH is a beneficial system as a solution to water scarcity on a large scale. This study explored the suitability of RWH in the water-scarce northwest region of Bangladesh, focusing on Rangpur and Rajshahi divisions. Using a Geographic Information System (GIS) integrated with the AHP, fourteen criteria, including nine bio-physical criteria and five socio-economic criteria, were given weight through the use of a pairwise comparison matrix. To determine the ideal normalized weight for both bio-physical and socio-economic criteria, four scenarios were generated through sensitivity analysis, from which the optimal potential zone for RWH was selected. This approach increased the reliability of the optimal site selection process. Analyzing fourteen criteria in a single pairwise comparison matrix is a complex process, but it shows the precise weight of each criterion based on its direct correlation with the others. From this direct correlation in this study, sensitivity analysis not only created different scenarios for RWH, but also showed that socio-economic criteria have a significant impact on creating RWH potential map. Among four scenarios, equal weight was given to bio-physical and socio-economic criteria (case 3) showed a better result in RWH potential mapping through validation. That optimal scenario illustrates that ‘poor’, ‘moderate’, and ‘good’ RWH potential zone occupies an area of 13251 km 2 (39%), 18687 km 2 (55%), and 2039 km 2 (6%) of the study area, respectively. Among the sixteen districts in this study area, Thakurgaon, Panchagarh, Kurigram, Lalmonirhat, and Nilphamari districts, along with some parts of Pabna district, show comparatively better RWH suitability. Based on the comparison of divisions, the Rangpur division dominates in the good RWH potential zone. Finally, runoff and rainfall among all bio-physical criteria and distance from agricultural land among all socio-economic criteria show the highest impact through regression analysis in the final output. This optimal RWH potential zone map will help the planner to choose a suitable location for building an RWH structure. Most of the suitable RWH zones have agricultural land nearby, demonstrating the necessity of such RWH structures that will benefit farmers in irrigation, such as farm ponds, nala bunds, etc. Additionally, RWH can be practiced through various methods, such as percolation tanks and rooftop harvesting systems for domestic uses. These structures are cost-effective and easily manageable. Furthermore, areas with a lower groundwater table, like the Rajshahi division, can have recharge wells built there. The next phase of research should involve analyzing specific RWH structure potential under certain conditions for this area, estimating RWH potential under shifting climate patterns, and loss analysis from the storage system. Thus, this study’s efficient methodology could be valuable for addressing water scarcity in other regions, especially in regions with similar bio-physical and socio-environmental conditions. Declarations Funding The authors declare that no funds, grants, or other support were received during the preparation of this manuscript. Competing Interests The authors declare that there are no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgements The authors would like to thank the two anonymous reviewers for their constructive comments, which improved the manuscript. Author Contributions Hera Dutta: Conceptualization, Methodology, Data curation, Software, Formal Analysis, Validation, Writing - original draft. Seheba Yameen: Conceptualization, Methodology, Data curation, Software, Formal Analysis, Validation, Writing - original draft. Mushfiqul Alam: Conceptualization, Data curation, Software, Writing - original draft. Pollen Chakma: Conceptualization, Data curation, Writing – review and editing. References Afsari, Navila, Sonia Binte Murshed, Sayed Mohammad Nazim Uddin, and Monzurul Hasan. 2022. “Opportunities and Barriers Against Successive Implementation of Rainwater Harvesting Options to Ensure Water Security in Southwestern Coastal Region of Bangladesh.” https://doi.org/10.3389/frwa.2022.811918. Ahmed, Sharif, Touhidul Islam, Abdul Haque, and Humnath Bhandari. 2023. “Effect of Rainfall Variability on Cropping Windows of Patuakhali and Rangpur Regions.” Akter, Aysha, and Shoukat Ahmed. 2015a. “Potentiality of Rainwater Harvesting for an Urban Community in Bangladesh.” Journal of Hydrology 528 (September):84–93. https://doi.org/10.1016/j.jhydrol.2015.06.017. Al-Adamat, Rida, Saad AlAyyash, Hani Al-Amoush, Odeh Al-Meshan, Zahir Rawajfih, Akram Shdeifat, Adnan Al-Harahsheh, and Mohammed Al-Farajat. 2012. “The Combination of Indigenous Knowledge and Geo-Informatics for Water Harvesting Siting in the Jordanian Badia.” Journal of Geographic Information System 04 (04): 366–76. https://doi.org/10.4236/jgis.2012.44042. Al-Adamat, Rida, Abdullah Diabat, and Ghada Shatnawi. 2010a. “Combining GIS with Multicriteria Decision Making for Siting Water Harvesting Ponds in Northern Jordan.” Journal of Arid Environments 74 (11): 1471–77. https://doi.org/10.1016/j.jaridenv.2010.07.001. Ali, Md. Hossain, Md. Hasanuzzaman, Md. Aktarul Islam, and Partha Biswas. 2022. “Groundwater Recharge Estimation at Barind Area, Bangladesh for Sustainable Groundwater Management: Application of Multiple Methods.” European Journal of Environment and Earth Sciences 3 (6): 23–29. https://doi.org/10.24018/ejgeo.2022.3.6.312. Al-Khuzaie, Marwah M., Hussein Janna, and Nadhir Al-Ansari. 2020a. “Assessment Model of Water Harvesting and Storage Location Using GIS and Remote Sensing in Al-Qadisiyah, Iraq.” Arabian Journal of Geosciences 13 (21). https://doi.org/10.1007/s12517-020-06154-4. Al-shabeeb, Abdel Rahman. 2016. “The Use of AHP within GIS in Selecting Potential Sites for Water Harvesting Sites in the Azraq Basin—Jordan.” Journal of Geographic Information System 08 (01): 73–88. https://doi.org/10.4236/jgis.2016.81008. AL-Shammari, Muthanna M.A., Ayser M. AL-Shamma’a, Ali Al Maliki, Hussain Musa Hussain, Zaher Mundher Yaseen, and Asaad M. Armanuos. 2021. “Integrated Water Harvesting and Aquifer Recharge Evaluation Methodology Based on Remote Sensing and Geographical Information System: Case Study in Iraq.” Natural Resources Research 30 (3): 2119–43. https://doi.org/10.1007/s11053-021-09835-3. Amin, M G Mostofa. 2005. “Rainwater Potential as a Supplemental Source for Dhaka City Supply.” https://www.researchgate.net/publication/258100609. Ammar, Adham, Michel Riksen, Mohamed Ouessar, and Coen Ritsema. 2016. “Identification of Suitable Sites for Rainwater Harvesting Structures in Arid and Semi-Arid Regions: A Review.” International Soil and Water Conservation Research . International Research and Training Center on Erosion and Sedimentation and China Water and Power Press. https://doi.org/10.1016/j.iswcr.2016.03.001. Aronoff, Stan. 1985. “The Minimum Accuracy Value as an Index of Classification Accuracy.” BADC “Minor Irrigation Survey Report 2014–15. Bangladesh Agricultural Development Corporation (BADC), Ministry of Agriculture, Government of the People’s Republic of Bangladesh. 2015.” Accessed June 30, 2025. https://badc.portal.gov.bd/. Balkhair, Khaled S., and Khalil Ur Rahman. 2021a. “Development and Assessment of Rainwater Harvesting Suitability Map Using Analytical Hierarchy Process, GIS and RS Techniques.” Geocarto International 36 (4): 421–48. https://doi.org/10.1080/10106049.2019.1608591. Bangladesh National Portal. “Government Information and Services.” Accessed July 4, 2025. bangladesh.gov.bd. BMD “Bangladesh Meteorological Department.” Accessed June 29, 2025. https://live6.bmd.gov.bd/. Buraihi, Faez Hussein, Abdul Rashid, and Mohamed Shariff. 2015. “Selection of Rainwater Harvesting Sites by Using Remote Sensing and GIS Techniques: A Case Study of Kirkuk, Iraq.” Vol. 76. http://www.usgs.gov/. BWDB. 2005. Rivers of Bangladesh . http://refhub.elsevier.com/S2589-7578(23)00022-7/rf0050. CHIRPS “Climate Hazards Group InfraRed Precipitation with Station Data.” Accessed June 29, 2025. https://www.chc.ucsb.edu/data/chirps. Chung, Chang-Jo F, and Andrea G Fabbri. 2003. “Validation of Spatial Prediction Models for Landslide Hazard Mapping.” Natural Hazards . Vol. 30. Congalton, Russell G. 1991. “A Review of Assessing the Accuracy of Classifications of Remotely Sensed Data.” Vol. 37. Darabi, Hamid, Ehsan Moradi, Ali Akbar Davudirad, Mohammad Ehteram, Artemi Cerda, and Ali Torabi Haghighi. 2021. “Efficient Rainwater Harvesting Planning Using Socio-Environmental Variables and Data-Driven Geospatial Techniques.” Journal of Cleaner Production 311 (August). https://doi.org/10.1016/j.jclepro.2021.127706. Das, Ashim C., Shihab A. Shahriar, Md A. Chowdhury, Md Lokman Hossain, Shahed Mahmud, Md Kamruzzaman Tusar, Romel Ahmed, and Mohammed Abdus Salam. 2023. “Assessment of Remote Sensing-Based Indices for Drought Monitoring in the North-Western Region of Bangladesh.” Heliyon 9 (2). https://doi.org/10.1016/j.heliyon.2023.e13016. DIVA-GIS “Data Interpolating Variational Analysis - Geographic Information System.” Accessed June 29, 2025. https://diva-gis.org/. Esri Sentinel-2. “Esri | Sentinel-2 Land Cover Explorer.” Accessed June 29, 2025. https://livingatlas.arcgis.com/landcoverexplorer/#mapCenter=92.06716%2C26.51464%2C12.56&mode=step&timeExtent=2017%2C2024&year=2024. FAO Soils Portal “Food and Agriculture Organization of the United Nations.” Accessed June 29, 2025. https://www.fao.org/soils-portal/data-hub/soil-maps-and-databases/faounesco-soil-map-of-the-world/en/. Ferozur, Rahaman Md, Chowdhury S. Jahan, Riad Arefin, and Quamrul H. Mazumder. 2019. “Groundwater Potentiality Study in Drought Prone Barind Tract, NW Bangladesh Using Remote Sensing and GIS.” Groundwater for Sustainable Development 8 (April):205–15. https://doi.org/10.1016/j.gsd.2018.11.006. Ferozur Rahaman, Mohammad. 2017. “Morphometric Analysis of Major Watersheds in Barind Tract, Bangladesh: A Remote Sensing and GIS-Based Approach for Water Resource Management.” Hydrology 5 (6): 86. https://doi.org/10.11648/j.hyd.20170506.12. García, M. J.López, and V. Caselles. 1991. “Mapping Burns and Natural Reforestation Using Thematic Mapper Data.” Geocarto International 6 (1): 31–37. https://doi.org/10.1080/10106049109354290. Gavhane, Kishor P., Anil Kumar Mishra, Arjamadutta Sarangi, Dhirendra Kumar Singh, and Susama Sudhishri. 2023. “Targeting of Rainwater Harvesting Structures Using Geospatial Tools and Analytical Hierarchy Process (AHP) in the Semi-Arid Region of Rajasthan (India).” Environmental Science and Pollution Research 30 (22): 61682–709. https://doi.org/10.1007/s11356-023-26289-7. GCN250 “Global Hydrologic Curve Number Dataset.” Accessed June 29, 2025. https://gee-community-catalog.org/projects/gcn250/. Goel, A. K., and R. Kumar. 2005. “Economic Analysis of Water Harvesting in a Mountainous Watershed in India.” Agricultural Water Management 71 (3): 257–66. https://doi.org/10.1016/j.agwat.2004.09.005. Hasan, Md Rashidul, Md Nuruzzaman, and Abdullah Al Mamun. 2019. “Contribution of Rainwater to the Irrigation Requirement for Paddy Cultivation at Tanore Upazila in Rajshahi, Bangladesh.” Air, Soil and Water Research 12 (April). https://doi.org/10.1177/1178622119837544. Hasan, Md Towhidul, Chowdhury Sarwar Jahan, Md Ferozur Rahaman, and Quamrul Hasan Mazumder. 2022. “Delineation of Zones and Sites for Artificial Recharge of Groundwater in Dry Land Barind Tract, Bangladesh Using MCDM Technique in GIS Environment.” Sustainable Water Resources Management 8 (5). https://doi.org/10.1007/s40899-022-00713-1. Hassan, Waqed H., Karrar Mahdi, and Zahraa K. Kadhim. 2025. “Optimal Rainwater Harvesting Locations for Arid and Semi-Arid Regions by Using MCDM-Based GIS Techniques.” Heliyon 11 (3). https://doi.org/10.1016/j.heliyon.2025.e42090. Hoque, Sonia F., and Robert Hope. 2020. “Examining the Economics of Affordability through Water Diaries in Coastal Bangladesh.” Water Economics and Policy 6 (3). https://doi.org/10.1142/S2382624X19500115. Huq, F F. 2020. “Impact of Groundwater Drought on Domestic Water Use in Barind Tract, Bangladesh.” J. Wat. Env. Sci 4:589–602. http://revues.imist.ma/?journal=jwes. HydroSHEDS. n.d. “The Hydrological Data and Maps Based on SHuttle Elevation Derivatives at Multiple Scales.” Accessed June 29, 2025. https://www.hydrosheds.org/. Islam, Riffat, and Md Kamruzzaman. 2015. “A Study on Urban Mobility in Northern Cities of Bangladesh.” Jacob Cohen, Patricia Cohen, Stephen G. West, and Leona S. Aiken. 2003. “Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences Third Edition.” Jenks, G.F. n.d. “Jenks, G.F., 1967. The Data Model Concept in Statistical Mapping. Int. Yearbook Cartography 7, 186–190.” Jha, Madan K., V. M. Chowdary, Y. Kulkarni, and B. C. Mal. 2014a. “Rainwater Harvesting Planning Using Geospatial Techniques and Multicriteria Decision Analysis.” Resources, Conservation and Recycling 83 (February):96–111. https://doi.org/10.1016/j.resconrec.2013.12.003. Karimi, Hamid, and Hossein Zeinivand. 2021. “Integrating Runoff Map of a Spatially Distributed Model and Thematic Layers for Identifying Potential Rainwater Harvesting Suitability Sites Using GIS Techniques.” Geocarto International 36 (3): 320–39. https://doi.org/10.1080/10106049.2019.1608590. Kränzlin, Irène. 2000. “Pond Management in Rural Bangladesh: Problems and Possibilities in the Context of the Water Supply Crisis.” Natural Resources Forum 24 (3): 211–23. https://doi.org/10.1111/j.1477-8947.2000.tb00945.x. Mahmood, Khalid, Ansab Qaiser, Sumar Farooq, and Mehr un Nisa. 2020. “RS- and GIS-Based Modeling for Optimum Site Selection in Rain Water Harvesting System: An SCS-CN Approach.” Acta Geophysica 68 (4): 1175–85. https://doi.org/10.1007/s11600-020-00460-x. Mahmoud, Shereif H., J. Adamowski, A. A. Alazba, and A. M. El-Gindy. 2016. “Rainwater Harvesting for the Management of Agricultural Droughts in Arid and Semi-Arid Regions.” Paddy and Water Environment 14 (1): 231–46. https://doi.org/10.1007/s10333-015-0493-z. Matomela, Nametso, Tianxin Li, and Harrison O. Ikhumhen. 2020a. “Siting of Rainwater Harvesting Potential Sites in Arid or Semi-Arid Watersheds Using GIS-Based Techniques.” Environmental Processes 7 (2): 631–52. https://doi.org/10.1007/s40710-020-00434-7. Mbilinyi, B. P., S. D. Tumbo, H. F. Mahoo, and F. O. Mkiramwinyi. 2007a. “GIS-Based Decision Support System for Identifying Potential Sites for Rainwater Harvesting.” Physics and Chemistry of the Earth 32 (15–18): 1074–81. https://doi.org/10.1016/j.pce.2007.07.014. NASA MODIS “NASA Moderate Resolution Imaging Spectroradiometer.” Accessed June 29, 2025. https://modis.gsfc.nasa.gov/. Nowreen, Sara, Imran Hossain Newton, Rashed Uz Zzaman, A. K.M.Saiful Islam, G. M.Tarekul Islam, and Md Saiful Alam. 2021. “Development of Potential Map for Groundwater Abstraction in the Northwest Region of Bangladesh Using RS-GIS-Based Weighted Overlay Analysis and Water-Table-Fluctuation Technique.” Environmental Monitoring and Assessment 193 (1). https://doi.org/10.1007/s10661-020-08790-5. Nyirenda, F., A. Mhizha, W. Gumindoga, and A. Shumba. 2021. “A Gis-Based Approach for Identifying Suitable Sites for Rainwater Harvesting Technologies in Kasungu District, Malawi.” Water SA 47 (3): 347–55. https://doi.org/10.17159/wsa/2021.v47.i3.11863. Oh, Hyun Joo, Seung Chan Ahn, Jong Kuk Choi, and Saro Lee. 2011. “Sensitivity Analysis for the GIS-Based Mapping of the Ground Subsidence Hazard near Abandoned Underground Coal Mines.” Environmental Earth Sciences 64 (2): 347–58. https://doi.org/10.1007/s12665-010-0855-1. Peters, Frank T., Olaf H. Drummer, and Frank Musshoff. 2007a. “Validation of New Methods.” Forensic Science International 165 (2–3): 216–24. https://doi.org/10.1016/j.forsciint.2006.05.021. Population & Housing Census Preliminary Report. 2022. “Bangladesh Bureau of Statistics and Informatics Division of Planning.” https://bbs.portal.gov.bd/. Qays Hashim, Hadeel, and Khamis Naba Sayl. 2020. “Detection of Suitable Sites for Rainwater Harvesting Planning in an Arid Region Using Geographic Information System.” https://doi.org/10.1007/s12518-020-00342-3/Published. Rahman, A T M Sakiur, Chowdhury Sarwar Jahan, Quamrul Hasan Mazumder, and Takahiro Hosono. 2017. “Drought Analysis and Its Implication in Sustainable Water Resource Management in Barind Area, Bangladesh.” Journal Geological Society of India . Vol. 89. Ramakrishnan, D, and K N Kusuma. 2009. “SCS-CN and GIS-Based Approach for Identifying Potential Water Harvesting Sites in the Kali Watershed, Mahi River Basin, India.” Rana, M. M.Shah Porun, and Md Moniruzzaman. 2023a. “A Combined GIS, Remote Sensing and MCDM Approach to Find Potential Location for Rainwater Harvesting Structure in Northwestern Part of Bangladesh.” HydroResearch 6 (January):235–46. https://doi.org/10.1016/j.hydres.2023.08.001. Rukunujjaman, Md. 2016. “Poverty Level of Bangladesh: Special Case, Rangpur and Rajshahi Division.” Vol. 5. www.ijsrm.humanjournals.comwww.ijsrm.humanjournals.com. Saaty, T.L., 1980. The Analytic Hierarchy Process. McGraw-Hill, New York.. Saaty, Thomas L., and Kevin P. Kearns. 1985. “The Analytic Hierarchy Process.” In Analytical Planning , 19–62. Elsevier. https://doi.org/10.1016/b978-0-08-032599-6.50008-8. Saha, Abir, Salahuddin Setu, Swadhin Das, Md Imran Hossain, AHM Khalequr Rahman, and Md Mafizur Rahman. 2024. “Decision Support System for Community Managed Rainwater Harvesting: A Case Study in the Salinity-Prone Coastal Region of Bangladesh.” Heliyon 10 (9). https://doi.org/10.1016/j.heliyon.2024.e30455. Sayl, K. N., A. S. Mohammed, and A. D. Ahmed. 2020. “GIS-Based Approach for Rainwater Harvesting Site Selection.” In IOP Conference Series: Materials Science and Engineering . Vol. 737. Institute of Physics Publishing. https://doi.org/10.1088/1757-899X/737/1/012246. Shadmehri Toosi, Amirhossein, Erfan Ghasemi Tousi, Seyed Ali Ghassemi, Ali Cheshomi, and Sina Alaghmand. 2020a. “A Multi-Criteria Decision Analysis Approach towards Efficient Rainwater Harvesting.” Journal of Hydrology 582 (March). https://doi.org/10.1016/j.jhydrol.2019.124501. Shahid, Shamsuddin, and Manzul Kumar Hazarika. 2010. “Groundwater Drought in the Northwestern Districts of Bangladesh.” Water Resources Management 24 (10): 1989–2006. https://doi.org/10.1007/s11269-009-9534-y. Shamsuzzoha, M, A Parvez, and A F M K Chowdhury. 2014. “Analysis of Variability in Rainfall Patterns in Greater Rajshahi Division Using GIS.” J. Environ. Sci. & Natural Resources . Vol. 7. Story, Michael, and Russell G Congalton. “Accuracy Assessment: A User’s Perspective.” Subramanya, K. 2008. Engineering Hydrology . Tariqul Islam, Md, Md Mohabbat Ullah, M. G. Mostofa Amin, and Sahadat Hossain. 2017. “Rainwater Harvesting Potential for Farming System Development in a Hilly Watershed of Bangladesh.” Applied Water Science 7 (5): 2523–32. https://doi.org/10.1007/s13201-016-0444-x. Tiwari, Kuldeep, Rohit Goyal, and Archana Sarkar. 2018. “GIS-Based Methodology for Identification of Suitable Locations for Rainwater Harvesting Structures.” Water Resources Management 32 (5): 1811–25. https://doi.org/10.1007/s11269-018-1905-9. Tract, Barind, Bangladesh Bazlar Rashid, Badrul Islam, Md Bazlar Rashid, and Bazlar Rashid. 2014. “Bazlar Rashid, Sultan-Ul-Islam, Badrul Islam. Drainage Characteristics and Evolution of the Barind Tract.” Bangladesh. American Journal of Earth Sciences . Vol. 1. http://www.openscienceonline.com/journal/ajes. USGS Earth Explorer “United States Geological Survey Earth Explorer.” Accessed June 29, 2025. https://www.usgs.gov/tools/earthexplorer. Victor Ponce, By M, and Richard H Hawkins. 1996. “Runoff Curve Number: Has it Reached Maturity?” Ward, S., F. A. Memon, and D. Butler. 2012. “Performance of a Large Building Rainwater Harvesting System.” Water Research 46 (16): 5127–34. https://doi.org/10.1016/j.watres.2012.06.043. Worldpop. “Open Spatial Demographic Data and Research.” Accessed June 29, 2025. https://www.worldpop.org/. Wu, Ray Shyan, Gabriela Lucia Letona Molina, and Fiaz Hussain. 2018a. “Optimal Sites Identification for Rainwater Harvesting in Northeastern Guatemala by Analytical Hierarchy Process.” Water Resources Management 32 (12): 4139–53. https://doi.org/10.1007/s11269-018-2050-1. Zlaugotne, Beate, Lauma Zihare, Lauma Balode, Antra Kalnbalkite, Aset Khabdullin, and Dagnija Blumberga. 2020. “Multi-Criteria Decision Analysis Methods Comparison.” Environmental and Climate Technologies 24 (1): 454–71. https://doi.org/10.2478/rtuect-2020-0028. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 23 Feb, 2026 Reviewers invited by journal 21 Jan, 2026 Editor invited by journal 09 Jan, 2026 Editor assigned by journal 26 Dec, 2025 First submitted to journal 25 Dec, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8449542","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":578131289,"identity":"37f02def-dd56-4674-b94e-4d5b66f93757","order_by":0,"name":"Hera Dutta","email":"","orcid":"","institution":"Chittagong University of Engineering and Technology","correspondingAuthor":false,"prefix":"","firstName":"Hera","middleName":"","lastName":"Dutta","suffix":""},{"id":578131290,"identity":"43acd577-f20c-4ca4-be43-317eae430a96","order_by":1,"name":"Seheba Yameen","email":"","orcid":"","institution":"Chittagong University of Engineering and Technology","correspondingAuthor":false,"prefix":"","firstName":"Seheba","middleName":"","lastName":"Yameen","suffix":""},{"id":578131291,"identity":"b920ec5f-379b-4916-97bc-5bee8863c7ba","order_by":2,"name":"Mushfiqul Alam","email":"","orcid":"","institution":"Chittagong University of Engineering and Technology","correspondingAuthor":false,"prefix":"","firstName":"Mushfiqul","middleName":"","lastName":"Alam","suffix":""},{"id":578131292,"identity":"20b0051a-110b-4064-b5da-f2c1c9a43ddc","order_by":3,"name":"Pollen Chakma","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9klEQVRIiWNgGAWjYFAC5sYDQDKBgQdIfoAJMjYw49HC2ADXwjgDyOIhSQszDzFazNkbGw583GGXx99zOvGzTc1hOXsG5ocfGHdY49Ri2XOw4eDMM8nFEmd7N0vnHDtszMPAZizBeCYdpxaDG4kNh3nbDiQ2nOfdIJ3bcDixh4HBjIGx7TB+LX+BWuaf593827LhcH0PA/s3wloYgVo2nO3dJs3YcDiBh4EHvy1gv/S2JSduPHN2m2XPsXTDnsM8xRKJePxizt588MHPNrvEeWdyN9/4UWMtz97evvHDRzwhZoApBIqRBJwasGoZBaNgFIyCUYAGAFhTXY5LSJvuAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0009-0007-3948-8735","institution":"Chittagong University of Engineering and Technology","correspondingAuthor":true,"prefix":"","firstName":"Pollen","middleName":"","lastName":"Chakma","suffix":""}],"badges":[],"createdAt":"2025-12-25 14:13:36","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8449542/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8449542/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":100971548,"identity":"86aca884-05dc-4031-b36b-cb0af9f3145e","added_by":"auto","created_at":"2026-01-23 10:13:30","extension":"xml","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":8399,"visible":true,"origin":"","legend":"","description":"","filename":"warmWARMD2503943.xml","url":"https://assets-eu.researchsquare.com/files/rs-8449542/v1/b0b31b21015779737b6ea1ae.xml"},{"id":100971553,"identity":"54efe3a6-48cd-45a5-bf4c-7f2a97838981","added_by":"auto","created_at":"2026-01-23 10:13:31","extension":"xml","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":966,"visible":true,"origin":"","legend":"","description":"","filename":"WARMD250394347768.go.xml","url":"https://assets-eu.researchsquare.com/files/rs-8449542/v1/5c0c0528a8f7b366d1ca1995.xml"},{"id":101202821,"identity":"26e28f92-1c34-432d-a64f-c6d89f6143f0","added_by":"auto","created_at":"2026-01-27 09:37:53","extension":"xml","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":788,"visible":true,"origin":"","legend":"","description":"","filename":"WARMD2503943Import.xml","url":"https://assets-eu.researchsquare.com/files/rs-8449542/v1/31ad91aee17596e0573b7a37.xml"},{"id":100971559,"identity":"26e24481-ae91-423f-a3d3-35057c6c95fe","added_by":"auto","created_at":"2026-01-23 10:13:32","extension":"xml","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":300092,"visible":true,"origin":"","legend":"","description":"","filename":"WARMD25039430enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-8449542/v1/14374c271f774cf006fe833a.xml"},{"id":100971511,"identity":"7f33513e-a121-4996-a60b-de347d822fbd","added_by":"auto","created_at":"2026-01-23 10:13:29","extension":"jpeg","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":711091,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8449542/v1/ccbfde4f6d4224e1df3283ed.jpeg"},{"id":100971591,"identity":"10c6166b-6367-4493-84bc-2a398a9845cf","added_by":"auto","created_at":"2026-01-23 10:13:35","extension":"jpeg","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":2835181,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage10.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8449542/v1/13c0d2eb250f2a87219cccbe.jpeg"},{"id":100971578,"identity":"12ddd66f-d6c8-441a-92ba-7c8ff21f8365","added_by":"auto","created_at":"2026-01-23 10:13:34","extension":"jpeg","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":280269,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage11.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8449542/v1/ea139866b3e661c460fa902a.jpeg"},{"id":100971546,"identity":"e2a06570-527d-4f03-8f4a-fd78c5edb0f1","added_by":"auto","created_at":"2026-01-23 10:13:30","extension":"jpeg","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":302517,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage12.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8449542/v1/f0d1fb003fbaaae945a14b39.jpeg"},{"id":100971550,"identity":"01c9f330-9e9c-44d6-8632-2c355002a290","added_by":"auto","created_at":"2026-01-23 10:13:31","extension":"jpeg","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":764736,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage13.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8449542/v1/905d4bb2bcba0708e73fb25c.jpeg"},{"id":100971512,"identity":"cc61c7f2-c943-48df-88e1-a3a7f2dd7e94","added_by":"auto","created_at":"2026-01-23 10:13:29","extension":"jpeg","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":351540,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage14.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8449542/v1/30b247c6178d9690118d972d.jpeg"},{"id":100971589,"identity":"0bf7af55-57e5-453c-a6d9-9a46552ea526","added_by":"auto","created_at":"2026-01-23 10:13:35","extension":"jpeg","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":898889,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage15.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8449542/v1/e6050d053154ba37be04a3dd.jpeg"},{"id":100971575,"identity":"f7737e5b-97f7-4f4e-b8d9-46c365f516fd","added_by":"auto","created_at":"2026-01-23 10:13:34","extension":"jpeg","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":811628,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage16.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8449542/v1/431d7e04f7c9db7bb52bb24d.jpeg"},{"id":100971563,"identity":"ec5f7531-2585-4902-815a-f01e01b8622e","added_by":"auto","created_at":"2026-01-23 10:13:33","extension":"jpeg","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":253082,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage17.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8449542/v1/b01edfdcfd5fe2067742f380.jpeg"},{"id":100971584,"identity":"86d0fa99-daec-45b8-b51d-190e55fcfa10","added_by":"auto","created_at":"2026-01-23 10:13:35","extension":"jpeg","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":448026,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8449542/v1/2e91d80d89d4cfd81c19bc37.jpeg"},{"id":100971577,"identity":"9c2b8237-0a1c-4e01-8c26-0180caa6e76c","added_by":"auto","created_at":"2026-01-23 10:13:34","extension":"emf","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1454420,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage3.emf","url":"https://assets-eu.researchsquare.com/files/rs-8449542/v1/d8dfe446075163dffe8e84d7.emf"},{"id":100971549,"identity":"f9ece5d2-f571-4592-989d-f4fd8327bea8","added_by":"auto","created_at":"2026-01-23 10:13:30","extension":"emf","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1454420,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage4.emf","url":"https://assets-eu.researchsquare.com/files/rs-8449542/v1/ac4e64e5aa062f7a56a8ab51.emf"},{"id":100971573,"identity":"f9c25850-882d-4ca1-b0e5-b6626f122438","added_by":"auto","created_at":"2026-01-23 10:13:33","extension":"jpeg","order_by":17,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":627755,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8449542/v1/4a745eb1842981b96d6f0440.jpeg"},{"id":101203831,"identity":"de26c4fc-7163-4734-89b1-b918b32f8ff4","added_by":"auto","created_at":"2026-01-27 09:40:46","extension":"jpeg","order_by":18,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1934711,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8449542/v1/2852e3b1ef36aa2f4d41afa4.jpeg"},{"id":101202880,"identity":"ad9057f9-2393-4be4-b49f-97d0a31b22db","added_by":"auto","created_at":"2026-01-27 09:38:01","extension":"jpeg","order_by":19,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":512444,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8449542/v1/f220cb7e4a8454bf122aed0f.jpeg"},{"id":100971574,"identity":"4f24e206-e72e-45b5-beb3-82bece9fc2a8","added_by":"auto","created_at":"2026-01-23 10:13:33","extension":"jpeg","order_by":20,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1837586,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage8.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8449542/v1/98864da034a17398fb0d16df.jpeg"},{"id":101296676,"identity":"78f60487-e5a2-4314-acd7-ce8e4e86481c","added_by":"auto","created_at":"2026-01-28 09:18:35","extension":"jpeg","order_by":21,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":277890,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage9.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8449542/v1/eda82e8208c883a839444eb8.jpeg"},{"id":100971579,"identity":"a6dda8a0-71ee-4a86-9a97-d6dd358b0e88","added_by":"auto","created_at":"2026-01-23 10:13:35","extension":"png","order_by":22,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":204563,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8449542/v1/3a1c01d28ad989c2aef60749.png"},{"id":100971560,"identity":"8c399cd7-51d1-4b83-ac18-9828763808d3","added_by":"auto","created_at":"2026-01-23 10:13:32","extension":"png","order_by":23,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":680983,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage10.png","url":"https://assets-eu.researchsquare.com/files/rs-8449542/v1/8c8621c252d5259b5faf89d6.png"},{"id":101203065,"identity":"22de65fa-eb66-4c15-8bc4-b5c0b824efc3","added_by":"auto","created_at":"2026-01-27 09:38:41","extension":"png","order_by":24,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":38437,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage11.png","url":"https://assets-eu.researchsquare.com/files/rs-8449542/v1/8e818a552295bde052972bba.png"},{"id":100971552,"identity":"6586ce9d-9a0d-42d3-bd4c-6fe53baf10b8","added_by":"auto","created_at":"2026-01-23 10:13:31","extension":"png","order_by":25,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":47786,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage12.png","url":"https://assets-eu.researchsquare.com/files/rs-8449542/v1/2e9f5c3226bb362fb3df09f2.png"},{"id":101202799,"identity":"de174feb-d2e3-4e7d-aa73-d8410de6b2ca","added_by":"auto","created_at":"2026-01-27 09:37:45","extension":"png","order_by":26,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":205429,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage13.png","url":"https://assets-eu.researchsquare.com/files/rs-8449542/v1/ea1a4f2c1dcf4d69d77c1a72.png"},{"id":100971566,"identity":"14b21a1f-2f5c-4393-a32a-af09f348b97c","added_by":"auto","created_at":"2026-01-23 10:13:33","extension":"png","order_by":27,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":41479,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage14.png","url":"https://assets-eu.researchsquare.com/files/rs-8449542/v1/e9ecfacb7584475945769f55.png"},{"id":101297510,"identity":"cb1672cd-cf08-4cd7-943f-e2434c61f48f","added_by":"auto","created_at":"2026-01-28 09:27:32","extension":"png","order_by":28,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":202525,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage15.png","url":"https://assets-eu.researchsquare.com/files/rs-8449542/v1/288ab73c106540822524f97d.png"},{"id":100971565,"identity":"796164bb-b1e5-4fcf-ad5b-d02ee7b691a5","added_by":"auto","created_at":"2026-01-23 10:13:33","extension":"png","order_by":29,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":175416,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage16.png","url":"https://assets-eu.researchsquare.com/files/rs-8449542/v1/e6e149aa4d8d13ed35bb56c3.png"},{"id":101202911,"identity":"bf789bc4-4ec4-4af1-aa1a-1494d68b5333","added_by":"auto","created_at":"2026-01-27 09:38:07","extension":"png","order_by":30,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":39313,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage17.png","url":"https://assets-eu.researchsquare.com/files/rs-8449542/v1/a6af7c18d71338fc41b31381.png"},{"id":101202893,"identity":"87e170bc-c366-405d-b7ed-0c1adef8f0a0","added_by":"auto","created_at":"2026-01-27 09:38:04","extension":"png","order_by":31,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":78352,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8449542/v1/4544b8602938047243c47ef5.png"},{"id":100971567,"identity":"8da2bfae-245e-43c3-ada9-a7ba462de114","added_by":"auto","created_at":"2026-01-23 10:13:33","extension":"png","order_by":32,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":17775,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8449542/v1/27c65742f06df3572cc2fa83.png"},{"id":100971582,"identity":"17e50cc8-6af3-4cc7-8383-4a8c009aee40","added_by":"auto","created_at":"2026-01-23 10:13:35","extension":"png","order_by":33,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":49102,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8449542/v1/4b7a93956f972389c009645b.png"},{"id":100971558,"identity":"73e03b15-77a9-4c66-9565-240f7e941553","added_by":"auto","created_at":"2026-01-23 10:13:32","extension":"png","order_by":34,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":146940,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8449542/v1/33a7e98f49d48c8201fe3c04.png"},{"id":100971588,"identity":"b8567ed7-f970-47ed-af13-367fffd2e03b","added_by":"auto","created_at":"2026-01-23 10:13:35","extension":"png","order_by":35,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":455657,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-8449542/v1/6e388f15bbc607799793dcff.png"},{"id":101203170,"identity":"bca2c143-11e3-497c-9f98-e88f973664c3","added_by":"auto","created_at":"2026-01-27 09:38:57","extension":"png","order_by":36,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":111852,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-8449542/v1/2bab5ea252fec4b8da438044.png"},{"id":100971562,"identity":"7a9de4b7-f9c2-4f29-9e56-60de40286cb2","added_by":"auto","created_at":"2026-01-23 10:13:32","extension":"png","order_by":37,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":376634,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-8449542/v1/4df8a3096343e38dffdb4ca2.png"},{"id":100971516,"identity":"a6ff0328-7a06-4ef7-bb67-1bc5fc57d8e5","added_by":"auto","created_at":"2026-01-23 10:13:30","extension":"png","order_by":38,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":54352,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-8449542/v1/6e11d07e0ddaacf64f989798.png"},{"id":100971590,"identity":"38713fd0-8b41-44cd-acae-93369c0649f8","added_by":"auto","created_at":"2026-01-23 10:13:35","extension":"xml","order_by":39,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":301343,"visible":true,"origin":"","legend":"","description":"","filename":"WARMD25039430structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8449542/v1/5474a6daceda7146a867148a.xml"},{"id":101751097,"identity":"39474df9-b804-45bc-b291-8379dba23c36","added_by":"auto","created_at":"2026-02-03 10:13:42","extension":"html","order_by":40,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":318842,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8449542/v1/e577e85c4e540c3137b2a999.html"},{"id":100971587,"identity":"69dcb6c7-6a79-4789-8118-fef339f45f00","added_by":"auto","created_at":"2026-01-23 10:13:35","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":711091,"visible":true,"origin":"","legend":"\u003cp\u003eAll districts located in the study area\u003c/p\u003e","description":"","filename":"1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8449542/v1/afe8a45aca40a26e736f6d45.jpeg"},{"id":101202797,"identity":"dab703b3-f67a-45bb-80aa-f52af0bd4529","added_by":"auto","created_at":"2026-01-27 09:37:45","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":448026,"visible":true,"origin":"","legend":"\u003cp\u003eFramework of the methodology to choose an optimal site for RWH\u003c/p\u003e","description":"","filename":"2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8449542/v1/79904986edb6994c3205c090.jpeg"},{"id":100971556,"identity":"b45752a1-b7ae-4010-870e-2f0becf0ff36","added_by":"auto","created_at":"2026-01-23 10:13:31","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":139960,"visible":true,"origin":"","legend":"\u003cp\u003eInput layers of criteria with corresponding feature class (a) Rainfall (b) Slope (c) Soil Type (d) Drainage Density\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8449542/v1/42a1dda10eaee619f792ce9e.jpg"},{"id":100971583,"identity":"6b8ee84c-7cf2-4b39-8160-47e7d324301f","added_by":"auto","created_at":"2026-01-23 10:13:35","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":156515,"visible":true,"origin":"","legend":"\u003cp\u003eInput layers of criteria with corresponding feature class (a) Runoff (b) Landuse Landcover (c) Evapotranspiration (d) Depression\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8449542/v1/dac7e82a5ab491fd612800c3.jpg"},{"id":101202974,"identity":"b697a054-3861-44d3-b766-ce4ef5143fb1","added_by":"auto","created_at":"2026-01-27 09:38:20","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":512444,"visible":true,"origin":"","legend":"\u003cp\u003eInput layers of criteria with corresponding feature class (a) Groundwater Table (b) Population Density\u003c/p\u003e","description":"","filename":"5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8449542/v1/5e7e288c39537a48a12aeeae.jpeg"},{"id":100971592,"identity":"e3bf3b68-b747-4faa-81d8-bc25098474bd","added_by":"auto","created_at":"2026-01-23 10:13:37","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1837586,"visible":true,"origin":"","legend":"\u003cp\u003eInput layers of criteria with corresponding feature class (a) Distance from Agricultural Land (b) Distance from Urban Area (c) Distance from Road (d) Distance from Stream\u003c/p\u003e","description":"","filename":"6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8449542/v1/948228dc716ddbab33a7e479.jpeg"},{"id":101203260,"identity":"224b42e5-c9ff-4744-9cb3-74322bf6bba3","added_by":"auto","created_at":"2026-01-27 09:39:13","extension":"jpeg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":277890,"visible":true,"origin":"","legend":"\u003cp\u003eGraph showing different criteria normalized weight for every scenario\u003c/p\u003e","description":"","filename":"7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8449542/v1/bc55a5a36080a0ff1b746dc3.jpeg"},{"id":101202949,"identity":"cb03e0b2-05dd-4eb3-b9ca-6e45fb4f1207","added_by":"auto","created_at":"2026-01-27 09:38:14","extension":"jpeg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":2835181,"visible":true,"origin":"","legend":"\u003cp\u003eRainwater harvesting potential zone map of the study area for all scenario (a) Base AHP (b) Case 1 (c) Case 2 (d) Case 3\u003c/p\u003e","description":"","filename":"8.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8449542/v1/65aec0f45a14397530abaafe.jpeg"},{"id":100971569,"identity":"74c567b9-ff7d-4b05-a251-74f44cc040db","added_by":"auto","created_at":"2026-01-23 10:13:33","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":90565,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of RWH potential class area between all scenarios (a) Entire study area (b) Rajshahi division (c) Rangpur division\u003c/p\u003e","description":"","filename":"9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8449542/v1/1ced7e1ce881e0f645306bf2.jpg"},{"id":100971514,"identity":"8c263c1b-e45c-421d-8e37-8a32845c546f","added_by":"auto","created_at":"2026-01-23 10:13:30","extension":"jpeg","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":764736,"visible":true,"origin":"","legend":"\u003cp\u003eRandom accuracy assessment points for validating RWH potential map of all scenario\u003c/p\u003e","description":"","filename":"10.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8449542/v1/5e7979c6334b9d016e9f325e.jpeg"},{"id":100971570,"identity":"60c4e77a-6457-4d97-832a-0f4a6547649c","added_by":"auto","created_at":"2026-01-23 10:13:33","extension":"jpeg","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":351540,"visible":true,"origin":"","legend":"\u003cp\u003eComparison between all accuracy indicators of RWH potential map for all scenarios\u003c/p\u003e","description":"","filename":"11.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8449542/v1/11298495f040d60ff49770d6.jpeg"},{"id":100971510,"identity":"f7c749b2-6923-4211-8731-8846b6e9bd2d","added_by":"auto","created_at":"2026-01-23 10:13:29","extension":"jpeg","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":898889,"visible":true,"origin":"","legend":"\u003cp\u003eRegression analysis between rainwater harvesting potential index and criteria (a) Rainfall (b) Slope (c) Drainage Density (d) Runoff (e) Evapotranspiration (f) Depression\u003c/p\u003e","description":"","filename":"12.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8449542/v1/4853588a0691c0fb2731713b.jpeg"},{"id":100971515,"identity":"bdc9dd8a-805b-43d7-b6f8-ddad7b9950ef","added_by":"auto","created_at":"2026-01-23 10:13:30","extension":"jpeg","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":811628,"visible":true,"origin":"","legend":"\u003cp\u003eRegression analysis between rainwater harvesting potential index and criteria (a) Groundwater Table (b) Population Density (c) Distance from Agricultural Land (d) Distance from Urban Area (e) Distance from Road (f) Distance from Stream\u003c/p\u003e","description":"","filename":"13.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8449542/v1/69e8eb61d7dff808e203be5e.jpeg"},{"id":101203379,"identity":"dd2d522b-25b5-4d17-9fac-d5228fdf8c56","added_by":"auto","created_at":"2026-01-27 09:39:30","extension":"jpeg","order_by":14,"title":"Figure 14","display":"","copyAsset":false,"role":"figure","size":253082,"visible":true,"origin":"","legend":"\u003cp\u003eMean rainwater harvesting potential index of each feature for criteria (a) Soil Type (b) Landuse Landcover\u003c/p\u003e","description":"","filename":"14.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8449542/v1/67e1f43f4cb333fd063691b0.jpeg"},{"id":101755910,"identity":"ec09fc53-fae5-47e5-b66a-be95ab708d3c","added_by":"auto","created_at":"2026-02-03 10:55:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":12419584,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8449542/v1/efead41b-ba1c-4d92-8151-26400cb23479.pdf"}],"financialInterests":"","formattedTitle":"Optimal site selection for rainwater harvesting through a combined approach of GIS and MCDM","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eFreshwater scarcity is gradually becoming a global concern due to rapid population growth, unsustainable resource use, and climate change impacts. In Bangladesh, the northwestern divisions of Bangladesh (Rangpur and Rajshahi) face acute water shortages due to chronic drought, irregular rainfall, and limited water surface availability (Mahmoud et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The topological feature of the northwestern zone makes this region\u0026rsquo;s climate arid, and as a result, drought occurs frequently (Das et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). With surface water decreasing, communities are becoming dependent on groundwater, resulting in aquifer depletion due to excessive agricultural extraction (Shahid and Hazarika 2010). Conventional water-reserving systems, such as ponds and canals, often fail to reserve water all year round. Improper maintenance and construction make these systems vulnerable to seasonal drying (Kr\u0026auml;nzlin \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). Moreover, the deep tubewells' installation and maintenance costs impose financial burdens on smallholder farmers (Hoque and Hope 2020). To face these challenges, RWH has vast significance in the sustainable water management practice (Ward et al. \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRWH is defined as collecting and storing rainwater that falls on the rooftops, open surfaces, and other catchment areas, and later using it to overcome water scarcity. This system can be fitted in the Northwestern zone of Bangladesh due to its abundant but poorly distributed rainfall. To reduce the unfavourable effects of droughts, depending on its suitability, RWH should be practiced efficiently (Gavhane et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In wet seasons, RWH enables communities to store water that can be used in dry periods, reducing pressure on aquifers and lessening dependence on costly irrigation systems (M. R. Hasan, Nuruzzaman, and Mamun 2019). It contributes to crop productivity, ensures early recovery of construction costs, and offers a low-cost, easy-to-maintain alternative with a high benefit-cost ratio (Goel and Kumar 2005; Amin \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Afsari et al. 2022).\u003c/p\u003e \u003cp\u003eThe selection of the RWH potential zone plays a vital role in increasing water availability in the semi-arid areas (Mbilinyi et al. 2007). Several studies in Bangladesh(Akter and Ahmed 2015; Rana and Moniruzzaman 2023; Tariqul Islam et al. \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) have explored the suitability of RWH. But most of the studies have been done considering bio-physical criteria (e.g., slope, land use, rainfall), often overlooking the socio-economic criteria (e.g., distance from water bodies, population density). Again, most of the studies all over the world(Shadmehri Toosi et al. 2020; Al-Adamat et al. 2010; Al-Adamat et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Al-shabeeb \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) emphasized on simple binary approach to assess socio-economic parameters, instead of applying weightings to socio-economic criteria based on their degrees of importance. Moreover, very few studies showed the difference in RWH planning, which is influenced by the emphasis between socio-economic and biophysical priorities.\u003c/p\u003e \u003cp\u003eMulti-criteria Decision-making process is a process used for making a transparent and structured decision for analysis by arranging different criteria in a suitable manner (Zlaugotne et al. \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). There are several MCDM methods, each having its own calculation and technique for analysis. Weighted Overlay Analysis (WOA) and Fuzzy Logic Model (FLM) are some methods used in MCDM, where WOA uses AHP and multiple influencing parameters for decision making analysis (Hassan et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Unlike many other methods, AHP enables comparisons on different parameters on a standardized scale and generates a consistency ratio, which helps to verify the accuracy of the evaluations (SAATY and KEARNS 1985).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLiterature review about the effects of different criteria on RWH Potential\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCriteria\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAuthors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEffect on RWH Potential\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCorrelation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"19\" rowspan=\"20\"\u003e \u003cp\u003eBiophysical Criteria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRainfall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(Shadmehri Toosi et al. 2020) ; (Jha et al. 2014)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigher annual rainfall is more favorable as it provides more water for harvesting.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eStrong\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSlope\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(Rana and Moniruzzaman 2023) ; (Shadmehri Toosi et al. 2020)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLow and medium slopes are more suitable for RWH structures as they facilitate water retention and reduce construction costs.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eStrong\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSoil Map\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(Jha et al. 2014)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eClayey soils generate more runoff and are suitable for harvesting.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eStrong\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(Qays Hashim and Naba Sayl, 2020)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMedium fine-grain soils are more suitable as they can retain a good amount of water.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGroundwater Depth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(AL-Shammari et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA shallow GW table is preferable for artificial recharge.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWeak\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSoil Depth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(Mbilinyi et al. 2007)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDeeper soils are more suitable for RWH as they can store more water.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWeak\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDrainage network/density\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(Buraihi, Rashid, and Shariff 2015)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLower drainage density has higher suitability because it accumulates water.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(Shadmehri Toosi et al. 2020)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigher drainage density helps runoff to flow continuously, which can be harvested immediately.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eRunoff potential map\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(Karimi and Zeinivand 2021)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigher runoff potential indicates more water available for collection.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eStrong\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(Balkhair and Ur Rahman 2021)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigher runoff depth is preferred over lower runoff depth.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLanduse Landcover\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(Karimi and Zeinivand 2021)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLULC is an important factor in surface runoff generation, considered as per the infiltration effect.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eStrong\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(AL-Shammari et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDepression map\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(Tiwari, Goyal, and Sarkar \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2018\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA large depression near the drainage network is preferable.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eElevation/DEM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(Sayl, Mohammed, and Ahmed \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDepends on the flow accumulation stream network and slope.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eStrong\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAspect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(Darabi et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eThe lower aspect is more suitable for RWH due to lower evaporation rates.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWeak\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTemperature\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(Darabi et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigher temperatures within a specific range enhance the probability of RWH.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eStrong\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStream order\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(Qays Hashim and Naba Sayl, 2020)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigher stream order is a sign of more tributaries, which is favorable for RWH.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGeology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(Mahmood et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDepends on other criteria. Such as soil type, elevation, LULC, etc.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eStrong\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEvapotranspiration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(Al-Khuzaie et al. 2020)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLower ET rates are more suitable for RWH.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eStrong\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEnvironmental factors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(Nyirenda et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMinimal environmental sensitivity is more suitable for RWH, posing less threat to ecosystems.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e \u003cp\u003eSocio-economic Criteria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDistance from agricultural land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(Qays Hashim and Naba Sayl, 2020)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProximity to farms enhances RWH water use for irrigation.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDistance from urban areas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(Qays Hashim and Naba Sayl, 2020)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProximity to urban areas reduces transportation costs.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDistance from roads / road network\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(Wu, Molina, and Hussain 2018)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProximity to roads improves accessibility and reduces transportation costs for water or materials.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDistance from well / river / stream\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(Matomela, Li, and Ikhumhen 2020a)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProximity to smaller streams ensures effective water collection.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eStrong\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDistance from fault parameters\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(Qays Hashim and Naba Sayl, 2020)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA greater distance from faults reduces the chance of RWH structure damage.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDistance from drainage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(Shadmehri Toosi et al. 2020)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDistance from drainage networks ensures cleaner water.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePopulation density\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(Darabi et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGreater population density elevates the demand for water resources.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eStrong\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHousehold income level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(Nyirenda et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigher-income households are more likely to maintain RWH technology.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWeak\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe main objective of this study is to identify the suitable zones for the RWH system by creating a potential map with GIS by integrating thematic layers, using AHP for decision making in the northwest part (Rangpur and Rajshahi divisions) of Bangladesh, and introducing four scenarios to specify suitable sites more accurately.\u003c/p\u003e \u003cp\u003eThe novelty of this study lies in selecting fourteen criteria for analysis, where runoff, depression, evapotranspiration, groundwater table, distance from agricultural land, distance from urban area, and distance from streams were newly added to this study area. Also, most of the previous research analyzed bio-physical and socio-economic criteria separately (Shadmehri Toosi et al. 2020; Al-Adamat et al. 2010; Al-Adamat et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Al-shabeeb \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), which ignored the different degrees of importance of socio-economic criteria. By conducting a Boolean approach for socio-economic criteria, those studies neglected the influence of all other criteria for a location where the Boolean value of any socio-economic criteria was zero, indicating a weak correlation between all criteria. But in this study, both types of criteria were analyzed collectively through AHP, to make a strong correlation among criteria and give significance to the degrees of importance of socio-economic criteria. Moreover, considering four scenarios for analysis: (a) according to normalized weights from pairwise comparison matrix, (b) priority of the bio-physical criteria, (c) priority of the socio-economic criteria, and (d) equal importance to both bio-physical criteria and socio-economic criteria ensured the gradual effect of socio-economic criteria and enhanced the specification for optimal site selection.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cp\u003eAll criteria were selected based on an assessment of the effect of the criteria analyzed in previous research and their relevance in this study (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Through a pairwise comparison matrix, all criteria were given weights and integrated in ArcGIS. Then, four scenarios were created by sensitivity analysis, and RWH potential map was generated for each scenario. Finally, using accuracy assessment, the optimal RWH potential map was selected. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the workflow diagram of the methodology used in this study.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Study area\u003c/h2\u003e \u003cp\u003eThe present study focuses on the northwestern zone of Bangladesh, specifically the divisions of Rangpur and Rajshahi (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), which fall within latitudes of 23\u0026deg;43'N to 26\u0026deg;38'N and longitudes of 88\u0026deg;00'E to 89\u0026deg;26'E. This region is geographically bordered by India on the west and north, and is also internally divided by administrative boundaries. The Rangpur division has an area of 16185.01 km\u003csup\u003e2\u003c/sup\u003e, and the Rajshahi division has an area of 18174.4 km\u003csup\u003e2\u003c/sup\u003e (Bangladesh National Portal; Rukunujjaman \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). 115 rivers in total, including 19 transboundary rivers, are located in the study area (Rana and Moniruzzaman 2023; BWDB,2005). The main major river, Padma, is situated in the southern part of the study area, which is bordered to the east by the Brahmaputra-Jamuna river system(Rana and Moniruzzaman 2023), and other minor seasonal streams are Atrai, Mohananda, Purnobhaba, etc (Tract et al. \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Ferozur et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The study area consists of sixteen districts, with eight districts in each division(Rukunujjaman \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This region exhibits three topographical features: Floodplains, Barind tract, and the Himalayan Piedmont Zone. The area lies largely within the Barind Tract, which is a raised landform above the surrounding floodplains. It is made of older sediments that were deposited in the Pleistocene era through natural processes like river erosion and deposition.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003ebetween 10\u0026deg;C and 20\u0026deg;C. Over time, a gradual cooling trend has been observed, with the average temperature decreasing by about 0.67\u0026deg;C each year (Nowreen et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). There is a gradual change in the slope in this area from west to east, varying from 0.70 m/km to 2.2 m/km (Ferozur et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). 80% of the annual rainfall occurs between May and September. Rajshahi typically receives 1542.1 mm to 2235.8 mm of average annual rainfall (Shamsuzzoha et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), and Rangpur receives approximately 2,268 mm of average annual rainfall (Ahmed et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Geologically, this area is dominated by Quaternary alluvial formations, while the Barind soils consist mostly of compact Pleistocene clay, which is known for its low permeability and poor water retention. Seasonal droughts frequently occur during pre-monsoon and post-monsoon in the Barind Tract. This leads to severe water scarcity when the surface water body shrinks in the dry season (Rahman et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Huq \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Ferozur Rahaman \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; M. T. Hasan et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The cities of these two divisions are based on two types, where Bogra, Dinajpur, and Rangpur are ranked as the most urbanized areas based on both street density and percentage of urban land for streets (Islam and Kamruzzaman \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe economy of the area is agriculture-based. 30% of the net cultivated area and 40% of the net irrigated area of Bangladesh are situated in this area; for example, one-third of the country\u0026rsquo;s total rice is cultivated in the northwestern region of Bangladesh (BADC). In many parts of the region, groundwater levels during the pre-monsoon season typically lie between 4 and 15 meters. However, in certain locations in the Barind area, the depth can reach around 34 meters. This makes it difficult for the farmers to get water for irrigation purposes (Ali et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). According to the Population and Housing Census Preliminary Report 2022, the population of Rangpur division was 17610955 persons, and for Rajshahi population was 20353116 persons. Though the population density of these two divisions is comparatively lower than other dense divisions of Bangladesh (Dhaka and Chattogram), due to drought, lowering of the groundwater table, and other phenomena, water scarcity is increasing day by day in this region. Considering the region\u0026rsquo;s challenging climate, groundwater conditions, irrigation purposes, and other demands, RWH can be a solution to meet the water needs of the local communities.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Data collection and pre-processing\u003c/h2\u003e \u003cp\u003eFor the RWH Potential map, fourteen variables were considered as influencing factors, which were classified as biophysical criteria and socio-economic criteria. The biophysical criteria included - Annual average Rainfall, Slope, Drainage Density, Soil Map, Runoff, Landuse Landcover (LULC), Evapotranspiration (ET), Topographic Depression, and Groundwater table depth. Socio-economic criteria consisted of Population density, Proximity to Urban areas, streams, roads, and agricultural lands.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThese data were collected from multiple sources, based on their relevance to the RWH system (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The annual average Rainfall data were collected from CHIPRSv. 03 for the years 2014 to 2024 with a spatial resolution of 5 kilometres. DEM (Digital Elevation Model)-based data, i.e., the Slope, Depression, and Distance from stream, were derived from SRTM 1 Arc-second DEM via the USGS Earth Explorer, which provides information at 30-meter spatial resolution globally. Pre-classified LULC datasets were derived from Sentinel-2 imagery with a spatial resolution of 10 meters, accessed via ESRI\u0026rsquo;s ArcGIS Living Atlas, which was further used in generating the map of distance from urban areas and agricultural lands. For runoff discharge calculation, Curve number data were obtained from the Global Hydrologic Curve Number dataset; Evapotranspiration data were obtained from MODIS MOD16A2 Version 6 using Google Earth Engine (GEE). The direct shapefile of the streams for the drainage density map was assessed from HydroSHEDS. The soil data was sourced from the FAO Soil portal. The population density data, shapefile of roads, and information were collected from WorldPop, DIVA-GIS, respectively. The groundwater table depth was collected in CSV format from the Bangladesh Water Development Board (BWDB). The DEM-based data, including Rainfall, Evapotranspiration, Landuse Landcover, and Curve Number data, were downloaded in TIFF format.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDetails about data collection and tools used in preparing the layers\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCriteria\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFormat\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSource\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eArcGIS Tools\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRainfall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2014\u0026ndash;2024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTIFF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCHIRPS v3.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIDW\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSlope\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTIFF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUSGS Earth Explorer (SRTM 1 ARC SECOND GLOBAL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSlope\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSoil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eShapefile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFAO Soils Portal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrainage Density\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eShapefile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHydroSHEDS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLine Density tool\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRunoff\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTIFF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGCN250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIDW\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLanduse Landcover\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTIFF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEsri Sentinel-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEvapotranspiration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2001\u0026ndash;2024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTIFF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNASA MODIS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIDW\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDepression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTIFF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUSGS Earth Explorer (SRTM 1 ARC SECOND GLOBAL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFill\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroundwater Table\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCSV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBWDB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIDW\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePopulation Density\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTIFF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWorldPop\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIDW\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistance from Agricultural land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTIFF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEsri Sentinel-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEuclidean Distance\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistance from Urban Area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTIFF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEsri Sentinel-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEuclidean Distance\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistance from Road\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eShapefile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDIVA-GIS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEuclidean Distance\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistance from Stream\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eShapefile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUSGS Earth Explorer (SRTM 1 ARC SECOND GLOBAL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEuclidean Distance\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAll the spatial layers were clipped to the study area extent, reprojected to the Universal Transverse Mercator (UTM) 45N coordinate system with WGS 1984 Datum, the raster layers were resampled to a common resolution of 30 meters, and no data values were set to zero during the analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Multi-criteria decision making approach\u003c/h2\u003e \u003cp\u003eIn this study, a well-known MCDM approach, called the Analytic Hierarchy Process (AHP) (Saaty \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e1980\u003c/span\u003e), was used to create correlations among the criteria. In any problem, judgment depends on certain criteria, which do not have the same impact. AHP principles are used to turn the relative importance of criteria into numbers. Different weights are given to certain criteria based on their relative importance, which can lead to a pairwise comparison matrix. Then, the normalized weight of all criteria is calculated, which shows the actual significance of each criterion in the decision-making process (Saaty \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e1980\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAll criteria (bio-physical and socio-economic) were selected as per the literature review and opinions from the experts with backgrounds in Civil Engineering and Water Resources Engineering. As the beginning step, an n \u0026times; n pairwise comparison matrix (where n is the number of criteria) was created. The weights of the criteria and their features were assigned using a 1 to 9 scale (equal importance to extreme importance)(Saaty \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e1980\u003c/span\u003e) by the experts considering the geological and social conditions of the study area (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cem\u003eM\u003c/em\u003e \u003csub\u003e \u003cem\u003en\u0026times;n\u003c/em\u003e \u003c/sub\u003e \u003cem\u003e=\u003c/em\u003e\u003c/p\u003e\u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eC\u003c/em\u003e\u003csub\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eC\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\dots\\:\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eC\u003c/em\u003e\u003csub\u003e\u003cem\u003en\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eC\u003c/em\u003e\u003csub\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{{w}_{1}}{{w}_{1}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{{w}_{1}}{{w}_{2}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\dots\\:\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{{w}_{1}}{{w}_{n}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eC\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{{w}_{2}}{{w}_{1}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{{w}_{2}}{{w}_{2}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\dots\\:\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{{w}_{2}}{{w}_{n}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\:\\:⋮\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\:\\:⋮\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\:\\:⋮\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\:\\:⋮\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eC\u003c/em\u003e\u003csub\u003e\u003cem\u003en\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{{w}_{n}}{{w}_{1}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{{w}_{n}}{{w}_{2}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\dots\\:\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{{w}_{n}}{{w}_{n}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eHere, M\u003csub\u003en\u0026times;n\u003c/sub\u003e is the pairwise comparison matrix (where n\u0026thinsp;=\u0026thinsp;14 for our study).\u003c/p\u003e \u003cp\u003eC\u003csub\u003e1\u003c/sub\u003e, C\u003csub\u003e2, \u0026hellip;,\u003c/sub\u003e C\u003csub\u003en\u003c/sub\u003e denote criteria, and w\u003csub\u003e1\u003c/sub\u003e, w\u003csub\u003e2\u003c/sub\u003e, \u0026hellip;, w\u003csub\u003en\u003c/sub\u003e denote the relative weight of each criterion.\u003c/p\u003e \u003cp\u003eThen the sum of each column of the matrix was calculated, and each entry of the matrix was divided by its column sum to derive the relative importance of the criteria, compared across columns:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:{\\stackrel{-}{M}}_{jk\\:}=\\frac{{M}_{jk}}{{\\sum\\:}_{i=1}^{n}{\\stackrel{-}{M}}_{ik}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eFinally, the normalized weight vector was determined by averaging the relative importance of the criteria across rows (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e):\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\:{N}_{j}\\:=\\:\\frac{{\\sum\\:}_{i=1}^{n}{\\stackrel{-}{M}}_{ji}}{n}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThe criteria normalized weight shows rainfall as the most impactful criterion and distance from urban area as the least impactful criterion (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo validate the normalized weight generated by AHP, the consistency ratio (CR) should be determined. The acceptable value of CR is equal to or less than 10%. A value of CR greater than 10% indicates that the pairwise matrix needs to be revised (Saaty \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e1980\u003c/span\u003e).\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$$\\:CR\\:=\\frac{CI}{RI}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere CI is the consistency index and RI is the random consistency. RI is a constant value for different numbers of criteria. In this study, RI\u0026thinsp;=\u0026thinsp;1.58 (for fourteen criteria).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePairwise comparison matrix used in AHP to show the relative significance of all criteria\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"15\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCriteria\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eST\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRO\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eET\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eDP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eGT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003ePD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eDA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003eDU\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c14\"\u003e \u003cp\u003eDR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c15\"\u003e \u003cp\u003eDS\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRainfall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSlope\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1/3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSoil Type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1/4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1/3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1/3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1/3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrainage Density\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1/4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1/3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1/3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1/3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRunoff\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1/3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLanduse Landcover\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1/3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEvapotranspiration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1/7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1/6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1/5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1/5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1/6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1/6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1/4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1/2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1/7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1/2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e1/7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDepression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1/7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1/6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1/5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1/5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1/6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1/6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1/4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1/2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1/7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1/2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e1/7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroundwater Table\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1/5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1/4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1/3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1/3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1/4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1/4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1/2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e\u0026frac12;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePopulation Density\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1/6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1/5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1/4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1/4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1/5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1/5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1/2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1/4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e\u0026frac14;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDist. from Agricultural Land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1/4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1/3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1/3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1/3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDist. from Urban Area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1/8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1/7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1/5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1/5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1/7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1/7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1/2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1/2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1/4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1/3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1/5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1/3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e1/9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDist. from Road\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1/6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1/5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1/4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1/4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1/5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1/5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1/2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1/4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e1/5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDist. from Stream\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1/4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1/3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1/3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1/3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eCI is determined from a formula:\u003cdiv id=\"Equ4\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ4\" name=\"EquationSource\"\u003e\n$$\\:CI\\:=\\frac{{\\lambda\\:}_{max}\\:-\\:n}{n-1}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e4\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\lambda\\:}_{\\text{m}\\text{a}\\text{x}}\\)\u003c/span\u003e\u003c/span\u003e is the principal eigenvector, determined by the eigenvector technique. The CR value determined from this study was 5.6% (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), which is acceptable.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCriteria normalized weight calculation following Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"16\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c16\" colnum=\"16\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCriteria\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eST\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRO\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eET\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eDP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eGT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003ePD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eDA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003eDU\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c14\"\u003e \u003cp\u003eDR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c15\"\u003e \u003cp\u003eDS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c16\"\u003e \u003cp\u003eCNW*\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e0.213\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e0.126\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e0.066\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e0.066\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e0.126\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e0.126\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eET\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e0.069\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e0.075\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"16\"\u003e*CNW\u0026thinsp;=\u0026thinsp;Criteria Normalized Weight\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"16\"\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\lambda\\:}_{\\text{m}\\text{a}\\text{x}}\\)\u003c/span\u003e\u003c/span\u003e = 15.162 \u0026amp; CR = 0.056\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Layer selection\u003c/h2\u003e \u003cp\u003eThe criteria for RWH systems were selected through a deep literature review, expert consultation, geological conditions of the study area, and data availability. From the previous literature review, it was observed that most of the studies focused on the biophysical criteria, mainly rainfall, slope, landuse landcover, and drainage density. Since 2000, the socio-economic criteria have been integrated into the studies of selecting sites for RWH systems (Ammar et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Shadmehri Toosi et al. 2020). In this study, an additional criterion, evapotranspiration, was used to make the analysis more precise and correct (Al-Khuzaie et al. 2020). The criteria are further described below.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.4.1. Bio-physical criteria\u003c/h2\u003e \u003cdiv id=\"Sec8\" class=\"Section4\"\u003e \u003ch2\u003e2.4.1.1. Rainfall\u003c/h2\u003e \u003cp\u003eThe most important and influential criterion recognized for the RWH system is rainfall(Ammar et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Jha et al., 2014; Rana \u0026amp; Moniruzzaman, 2023; Shadmehri Toosi et al., 2020). The more the rainfall is, the higher the potential for RWH systems in an area, since it impacts the sub-surface water storage and potential runoff(Ammar et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Rana \u0026amp; Moniruzzaman, 2023). The inverse distance weight (IDW) spatial analyst tool was used to create the rainfall map in the studied area. The highest and lowest rainfall were observed to be in the range of 1455 mm and 3553.5 mm, respectively. This rainfall dataset was compared with data from the Bangladesh Meteorological Department (BMD). The rainfall range was further classified into 5 classes (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). Rainfall less than 1500 mm was weighted as 3, and rainfall greater than 2500 mm was weighted as 9, as the higher the rainfall, the higher the potential for RWH systems. Meanwhile, the ranges,1501\u0026ndash;1800 mm, 1801\u0026ndash;2100 mm, and 2101-2500mm were given weights of 4, 5, and 7, respectively.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section4\"\u003e \u003ch2\u003e2.4.1.2. Slope\u003c/h2\u003e \u003cp\u003eThe slope of the terrain is one of the most important criteria for selecting sites for RWH, as the runoff of rainwater and infiltration of soil depend on it directly (Shadmehri Toosi et al. 2020). The slope map was generated in ArcGIS 10.8 using the slope analyst tool on the Digital Elevation Model (DEM) of the study area. The mosaic tool was used to combine all the raster files before extracting the study area. The slope map was classified into 0\u0026ndash;1, 1.1-3, 3.1-5, 5.1\u0026ndash;10, and greater than 10 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). According to the suitability based on the water accumulation and expert opinion, these classes were weighted as 9, 8, 7, 5, and 1, respectively, where it showed that lower slopes were considered to be suitable for selecting RWH sites.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section4\"\u003e \u003ch2\u003e2.4.1.3. Soil type\u003c/h2\u003e \u003cp\u003eInfiltration characteristics of a soil affect the potential run-off of an area. More impermeability of a soil increases the capacity to store water and enhances the retention of rainwater (Shadmehri Toosi et al. 2020; Jha et al. 2014). The areas with the most clayey soils are recommended for the selection of RWH zones, as clayey soils are the most impermeable and can retain rainwater for a longer time. The study area was classified into five types of soil classes, including clay, loam, sandy clay loam, sandy loam, and water (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec), which were weighted as 9, 5, 7, 3, and 1, respectively. The clay-type soil was assigned a higher weight as this soil type is highly suitable for the RWH system.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section4\"\u003e \u003ch2\u003e2.4.1.4. Drainage Density\u003c/h2\u003e \u003cp\u003eStoring rainwater involves the drainage density of an area, as lower drainage density indicates a lower amount of surface runoff loss and higher storage capacity of water. The lower the density the suitability of RWH reduces and vice versa(Jha et al., 2014). The mosaic tool was used to combine raster files before extracting the study area, and by using the Line Density Spatial Analyst Tool in ArcGIS 10.8, the final drainage density map was generated. The drainage density map was generated using the Line Density Spatial Analyst Tool in ArcGIS 10.8. The range of the drainage density was classified into 3 classes (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed). Drainage density less than 0.2 km/sq. km was assigned a weight of 8 as it indicates a more suitable condition for RWH; density range of 0.21 to 4 km/sq. km was given a weight of 5, indicating moderate suitability, and drainage density greater than 0.4 km/sq. km was assigned a weight of 3, which shows that the area is not suitable for RWH.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section4\"\u003e \u003ch2\u003e2.4.1.5. Runoff\u003c/h2\u003e \u003cp\u003eRunoff indicates the excess rainfall that does not infiltrate into the soil and is available for surface flow. In this study, the higher the rate of runoff, the more weight was assigned to it. The runoff map was generated using the mostly used SCS-CN method (Subramanya \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Ramakrishnan and Kusuma \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). The runoff depth was calculated using the formula:\u003cdiv id=\"Equ5\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ5\" name=\"EquationSource\"\u003e\n$$\\:Q=\\frac{{\\left(P-\\lambda\\:S\\right)}^{2}}{P+\\left(1-\\lambda\\:\\right)S}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e5\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ6\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ6\" name=\"EquationSource\"\u003e\n$$\\:S=\\frac{25400}{CN}-254$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e6\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere, \u003cem\u003eQ\u0026thinsp;=\u003c/em\u003e\u0026thinsp;Runoff Depth (mm)\u003c/p\u003e \u003cp\u003e \u003cem\u003eP\u0026thinsp;=\u003c/em\u003e\u0026thinsp;Amount of rainfall (mm)\u003c/p\u003e \u003cp\u003e \u003cem\u003eS\u0026thinsp;=\u003c/em\u003e\u0026thinsp;Potential maximum retention\u003c/p\u003e \u003cp\u003e \u003cem\u003eλ\u0026thinsp;=\u003c/em\u003e\u0026thinsp;Initial abstraction ratio\u003c/p\u003e \u003cp\u003e \u003cem\u003eCN\u0026thinsp;=\u003c/em\u003e\u0026thinsp;Curve Number\u003c/p\u003e \u003cp\u003eThe value of λ was taken as the standard value of 0.2 (Subramanya \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Victor Ponce and Hawkins 1996). Both the curve number map and the rainfall map were resampled to 30m spatial resolution and projected to the Universal Transverse Mercator (UTM) 45N coordinate system with WGS 1984 Datum. Further, the runoff rate was obtained by,\u003cdiv id=\"Equ7\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ7\" name=\"EquationSource\"\u003e\n$$\\:Q\\left(discharge\\right)=\\frac{Q\\left(depth\\right)*Area}{Time}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e7\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThe highest amount of runoff was about 2.8 m\u003csup\u003e3\u003c/sup\u003e/sec. The runoff map was then reclassified into 0\u0026ndash;1, 1.1-2, and 2\u0026lt; (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). Each of them was assigned a weight of 3, 6, and 7, respectively, which shows that the higher the runoff rate the suitability of RWH increases.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section4\"\u003e \u003ch2\u003e2.4.1.6. Landuse Landcover\u003c/h2\u003e \u003cp\u003eDifferent types of landuse have different impacts on selecting RWH zones. The landuse landcover map was directly obtained from Esri Sentinel in TIFF format in 10m resolution, which was verified using Google Earth Pro. It was further resampled to a 30m spatial resolution and reclassified (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb). Landuse where the chances of infiltration were high, like trees and flooded vegetation, was assigned a lower and moderate weight, respectively (Shadmehri Toosi et al. 2020). It is nearly impossible to build a structure in water for the RWH system, and the \u003c/p\u003e \u003cp\u003e \u003cb\u003e(a) (b)\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003esuitability for constructing RWH structures in built-up areas is less favorable, as it affects the daily human activities and existing infrastructures. So, these landuse were assigned a lower weight in terms of other classes. The best suitable sites are selected near cultivated land and bare ground, which were weighted as 8 and 9, respectively.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section4\"\u003e \u003ch2\u003e2.4.1.7. Evapotranspiration\u003c/h2\u003e \u003cp\u003eAs evapotranspiration reduces the volume of water stored, it influences RWH fashionably (Shadmehri Toosi et al. 2020; AL-Shammari et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). A smaller amount of evapotranspiration increases the suitability of RWH. The evapotranspiration map was reclassified into five classes (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec). The ranges were \u0026lt;\u0026thinsp;300 mm, 301\u0026ndash;500 mm, 501\u0026ndash;700 mm, 701\u0026ndash;900 mm, and 900\u0026thinsp;\u0026lt;\u0026thinsp;mm, which were given weights as 9, 7, 5, 3, and 1, respectively.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section4\"\u003e \u003ch2\u003e2.4.1.8. Depression\u003c/h2\u003e \u003cp\u003eAreas with a higher volume of depressions are highly suitable for harvesting rainwater as they catch a sufficient amount of runoff volume and reduce evaporation losses (Tiwari et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Moreover, areas with depression are more suitable for installing ponds and recharge pits. The depression map was generated using the fill tool in ArcGIS 10.8 and reclassified into 3 classes, which ranged from 0\u0026ndash;2 m, 2.1-5 m, and 5 m\u0026lt; (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed), and their assigned weight were 1, 3, and 5, respectively.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section4\"\u003e \u003ch2\u003e2.4.1.9. Groundwater Table\u003c/h2\u003e \u003cp\u003eA deeper groundwater table enhances the facility of rainwater to infiltrate through soil, which increases the suitability of constructing any artificial recharge-based RWH structure (AL-Shammari et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The IDW spatial analyst tool was used to determine the continuous groundwater depth in the study area. Groundwater table depth (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea) less than 5 meters was given a weight of 1, and greater than 15 meters was assigned as 9. The intermediate ranges 5.1 -8, 8.1\u0026ndash;12, and 12.1\u0026ndash;15 meters were given a weight of 2, 4, and 6, respectively.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003e2.4.2. Socio-economic Criteria\u003c/h2\u003e \u003cdiv id=\"Sec18\" class=\"Section4\"\u003e \u003ch2\u003e2.4.2.1. Population Density\u003c/h2\u003e \u003cp\u003eAreas with higher population density are more suitable for harvesting rainwater, as the water can be utilized properly and meet the demand of the people. (Darabi et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).The IDW spatial analyst tool was used to generate the population density map of the study area. The population density was further classified into five classes: less than 900, 901\u0026ndash;920, 921\u0026ndash;950, 951\u0026ndash;1000, and greater than 1000 persons/sq. km (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb). The assigned weights to these classes were 5,6,7,8, and 9, respectively.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section4\"\u003e \u003ch2\u003e2.4.2.2. Distance from agricultural land\u003c/h2\u003e \u003cp\u003eFor the irrigation of lands, the RWH harvesting sites should be near cultivated lands (Qays Hashim and Naba Sayl 2020). The distances (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea) less than 500 meters are weighted as 9, and greater than 2000 meters are weighted as 1, as the proximity of the water source ensures efficient use of water for crop production.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section4\"\u003e \u003ch2\u003e2.4.2.3. Distance from urban area\u003c/h2\u003e \u003cp\u003eDue to insufficient space and difficulties in construction, and the risk of flooding, areas located at a greater distance from urban areas are more suitable for RWH (Qays Hashim and Naba Sayl 2020). The distances were classified into five groups: \u0026lt;1000 m, 1001\u0026ndash;2000 m, 2001\u0026ndash;3000 m, 3000\u0026ndash;4000 m, and 4000m \u0026lt; (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb). Since the suitability increases as the distance increases from an urban area, distances greater than 4000m were assigned a weight of 7, and other classifications were weighted according to their significance.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section4\"\u003e \u003ch2\u003e2.4.2.4. Distance from road\u003c/h2\u003e \u003cp\u003eA greater distance from the roads can increase the transportation cost while constructing any RWH structures (Rana and Moniruzzaman 2023; Qays Hashim and Naba Sayl 2020). Considering the following facts, the distances from roads were classified into five classes: \u0026lt;1500 meters, 1501\u0026ndash;3000 meters, 3001\u0026ndash;4500 meters, 4501\u0026ndash;6000 meters, and 6000\u0026thinsp;\u0026lt;\u0026thinsp;meters (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ec). The ranges were assigned a weight of 2, 9, 7, 5, and 3. The map was generated using the Euclidean Distance tool in ArcGIS 10.8.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section4\"\u003e \u003ch2\u003e2.4.2.5. Distance from stream\u003c/h2\u003e \u003cp\u003eThe map of the distance from streams was generated using the Euclidean distance tool in ArcGIS. As the distance increases, the availability of water reduces (Matomela et al. 2020). Therefore, areas that are distant from a stream are considered more suitable for RWH sites as the sites have limited access to natural water resources. The distances were reclassified into five classes (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ed). Distance from stream less than 2000 meters were assigned a weight of 1, and distances greater than 8000 meters were assigned as 9. The intermediate ranges were given weight based on their relative importance.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Sensitivity analysis\u003c/h2\u003e \u003cp\u003eSensitivity analysis recognizes the importance of any factor or parameter by observing the change in the result after adjusting the value of some parameters. This analysis finds the sensitive parameters for an approach (Oh et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Balkhair and Ur Rahman 2021; Wu et al. 2018). In this study, a sensitivity analysis was conducted on the criteria type (bio-physical and socio-economic). From the pairwise comparison matrix, three different scenarios were created by changing the normalized weight of both criteria types to a certain ratio, named as \u0026lsquo;case \u003cspan refid=\"FPar1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u0026rsquo; (scenario 2), \u0026lsquo;case 2\u0026rsquo; (scenario 3), and \u0026lsquo;case 3\u0026rsquo; (scenario 4) (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). On the other hand, the scenario that was created from the correlation of criteria through the pairwise comparison matrix was named \u0026lsquo;base AHP\u0026rsquo; (scenario 1). In case \u003cspan refid=\"FPar1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the assigned normalized weight of bio-physical criteria was three times larger compared to socio-economic criteria. In case 2, the assigned normalized weight of socio-economic criteria was three times larger than bio-physical criteria. In case 3, the assigned normalized weight of both bio-physical and socio-economic criteria was equal. The consistency ratio of all cases was within the acceptable range. Figure\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e represents the changing pattern of criteria normalized weight based on different scenario.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCriteria normalized weight for all different scenario based on sensitivity analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCriteria Type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCriteria\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBase AHP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCase \u003cspan refid=\"FPar1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCase 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCase 3\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"8\" rowspan=\"9\"\u003e \u003cp\u003eBio-physical\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRainfall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.134\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSlope\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.079\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSoil Type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDrainage Density\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRunoff\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.079\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLanduse Landcover\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.079\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEvapotranspiration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDepression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGroundwater Table\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eSocio-economic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePopulation Density\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.088\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.059\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDistance from Agricultural Land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.252\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.168\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDistance from Urban Area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDistance from Road\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.088\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.059\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDistance from Stream\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.274\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.183\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e2.6. RWH potential zone mapping\u003c/h2\u003e \u003cp\u003eTo get the final output map, a GIS-based approach, combined with the MCDM process, was taken. The Weighted Linear Combination (WLC) method was used in ArcGIS to make a combination of all thematic layers according to their impact for the identification of the RWH potential zone. WLC is a simple method for the assessment of RWH potential zone and has greater acceptability in previous studies (Al-Adamat et al. 2010; Shadmehri Toosi et al. 2020; Akter and Ahmed 2015).\u003c/p\u003e \u003cp\u003eEvery criterion has different kinds of features. So, the relative weight of each feature of each criterion was given according to their importance using a scale of 1 to 9, and the normalized weight was calculated, which is shown in the Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. All weights were decided through a literature review and expert opinion (Rana and Moniruzzaman 2023; Jha et al. 2014; Shadmehri Toosi et al. 2020; Saha et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Al-Adamat, Diabat, and Shatnawi 2010; Matomela, Li, and Ikhumhen 2020). Then, the thematic layers were processed in ArcGIS based on the normalized\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eweight of their features. Thus, a simple mathematical equation was used in the ArcGIS environment to create the RWH potential zone map for this study:\u003c/p\u003e \u003cp\u003e \u003cem\u003eRWHPI = (RF)\u003c/em\u003e \u003csub\u003e \u003cem\u003ec\u003c/em\u003e \u003c/sub\u003e \u003cem\u003e(RF)\u003c/em\u003e \u003csub\u003e \u003cem\u003ef\u003c/em\u003e \u003c/sub\u003e \u003cem\u003e+ (SL)\u003c/em\u003e\u003csub\u003e\u003cem\u003ec\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e(SL)\u003c/em\u003e\u003csub\u003e\u003cem\u003ef\u003c/em\u003e\u003c/sub\u003e \u003cem\u003e+ (ST)\u003c/em\u003e\u003csub\u003e\u003cem\u003ec\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e(ST)\u003c/em\u003e\u003csub\u003e\u003cem\u003ef\u003c/em\u003e\u003c/sub\u003e \u003cem\u003e+ (DD)\u003c/em\u003e\u003csub\u003e\u003cem\u003ec\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e(DD)\u003c/em\u003e\u003csub\u003e\u003cem\u003ef\u003c/em\u003e\u003c/sub\u003e \u003cem\u003e+ (RO)\u003c/em\u003e\u003csub\u003e\u003cem\u003ec\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e(RO)\u003c/em\u003e\u003csub\u003e\u003cem\u003ef\u003c/em\u003e\u003c/sub\u003e \u003cem\u003e+ (LL)\u003c/em\u003e\u003csub\u003e\u003cem\u003ec\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e(LL)\u003c/em\u003e\u003csub\u003e\u003cem\u003ef\u003c/em\u003e\u003c/sub\u003e \u003cem\u003e+ (ET)\u003c/em\u003e\u003csub\u003e\u003cem\u003ec\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e(ET)\u003c/em\u003e\u003csub\u003e\u003cem\u003ef\u003c/em\u003e\u003c/sub\u003e \u003cem\u003e+ (DP)\u003c/em\u003e\u003csub\u003e\u003cem\u003ec\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e(DP)\u003c/em\u003e\u003csub\u003e\u003cem\u003ef\u003c/em\u003e\u003c/sub\u003e \u003cem\u003e+ (GT)\u003c/em\u003e\u003csub\u003e\u003cem\u003ec\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e(GT)\u003c/em\u003e\u003csub\u003e\u003cem\u003ef\u003c/em\u003e\u003c/sub\u003e \u003cem\u003e+ (PD)\u003c/em\u003e\u003csub\u003e\u003cem\u003ec\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e(PD)\u003c/em\u003e\u003csub\u003e\u003cem\u003ef\u003c/em\u003e\u003c/sub\u003e \u003cem\u003e+ (DA)\u003c/em\u003e\u003csub\u003e\u003cem\u003ec\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e(DA)\u003c/em\u003e\u003csub\u003e\u003cem\u003ef\u003c/em\u003e\u003c/sub\u003e \u003cem\u003e+ (DU)\u003c/em\u003e\u003csub\u003e\u003cem\u003ec\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e(DU)\u003c/em\u003e\u003csub\u003e\u003cem\u003ef\u003c/em\u003e\u003c/sub\u003e \u003cem\u003e+ (DR)\u003c/em\u003e\u003csub\u003e\u003cem\u003ec\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e(DR)\u003c/em\u003e\u003csub\u003e\u003cem\u003ef\u003c/em\u003e\u003c/sub\u003e \u003cem\u003e+ (DS)\u003c/em\u003e\u003csub\u003e\u003cem\u003ec\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e(DS)\u003c/em\u003e\u003csub\u003e\u003cem\u003ef\u003c/em\u003e\u003c/sub\u003e (8)\u003c/p\u003e \u003cp\u003ewhere \u003cem\u003eRWHPI\u003c/em\u003e represents the RWH Potential Index in this study area, the criteria normalized weight is denoted by subscript \u003cem\u003ec\u003c/em\u003e, and the normalized weight of a feature of each criterion is denoted by subscript \u003cem\u003ef\u003c/em\u003e(Shadmehri Toosi et al. 2020) and:\u003c/p\u003e \u003cp\u003e \u003cem\u003eRF\u0026thinsp;=\u003c/em\u003e\u0026thinsp;Rainfall criteria\u003c/p\u003e \u003cp\u003e \u003cem\u003eSL\u0026thinsp;=\u003c/em\u003e\u0026thinsp;Slope criteria\u003c/p\u003e \u003cp\u003e \u003cem\u003eST\u0026thinsp;=\u003c/em\u003e\u0026thinsp;Soil Type criteria\u003c/p\u003e \u003cp\u003e \u003cem\u003eDD\u0026thinsp;=\u003c/em\u003e\u0026thinsp;Drainage Density criteria\u003c/p\u003e \u003cp\u003e \u003cem\u003eRO\u0026thinsp;=\u003c/em\u003e\u0026thinsp;Runoff criteria\u003c/p\u003e \u003cp\u003e \u003cem\u003eLL\u0026thinsp;=\u003c/em\u003e\u0026thinsp;Landuse Landcover criteria\u003c/p\u003e \u003cp\u003e \u003cem\u003eET\u0026thinsp;=\u003c/em\u003e\u0026thinsp;Evapotranspiration criteria\u003c/p\u003e \u003cp\u003e \u003cem\u003eDP\u0026thinsp;=\u003c/em\u003e\u0026thinsp;Depression criteria\u003c/p\u003e \u003cp\u003e \u003cem\u003eGT\u0026thinsp;=\u003c/em\u003e\u0026thinsp;Groundwater Table criteria\u003c/p\u003e \u003cp\u003e \u003cem\u003ePD\u0026thinsp;=\u003c/em\u003e\u0026thinsp;Population Density criteria\u003c/p\u003e \u003cp\u003e \u003cem\u003eDA\u0026thinsp;=\u003c/em\u003e\u0026thinsp;Distance from Agricultural Land criteria\u003c/p\u003e \u003cp\u003e \u003cem\u003eDU\u0026thinsp;=\u003c/em\u003e\u0026thinsp;Distance from Urban Area criteria\u003c/p\u003e \u003cp\u003e \u003cem\u003eDR\u0026thinsp;=\u003c/em\u003e\u0026thinsp;Distance from Road criteria\u003c/p\u003e \u003cp\u003e \u003cem\u003eDS\u0026thinsp;=\u003c/em\u003e\u0026thinsp;Distance from Stream criteria\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssigned feature weights and justification for criteria used in RWH potential mapping\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCriteria\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFeature Class\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWeight\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNormalized Weight\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReason\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eRainfall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;1500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eMore rainfall, more suitability\u003c/p\u003e \u003cp\u003e(more rainfall provides more water for harvesting)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1501\u0026ndash;1800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1801\u0026ndash;2100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2101\u0026ndash;2500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2500\u0026lt;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eSlope\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eLess slope, more suitability\u003c/p\u003e \u003cp\u003e(based on water accumulation)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.1-3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.1-5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.1\u0026ndash;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u0026lt;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eSoil Type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClay\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eMore impermeable layer, more suitability\u003c/p\u003e \u003cp\u003e(it can store more water)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLoam\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSandy Clay Loam\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSandy Loam\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWater\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eDrainage Density\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eLess drainage density, more suitability\u003c/p\u003e \u003cp\u003e(better water accumulation, less runoff loss)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.21\u0026ndash;0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.4\u0026lt;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eRunoff\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eMore runoff, more suitability\u003c/p\u003e \u003cp\u003e(more water available for collection)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.1-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u0026lt;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003eLanduse Landcover\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWater\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003eThe different feature shows different suitability\u003c/p\u003e \u003cp\u003e(based on structure creation)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFlooded Vegetation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCultivated Land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBuilt-up Area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBare Ground\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWetland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eEvapotranspiration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eLess evapotranspiration, more suitability\u003c/p\u003e \u003cp\u003e(because it reduces the amount of water)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e301\u0026ndash;500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e501\u0026ndash;700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e701\u0026ndash;900\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e900\u0026lt;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eDepression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eMore depression, more suitability\u003c/p\u003e \u003cp\u003e(for natural storage)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.1-5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u0026lt;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eGroundwater Table\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eMore depth, more suitability\u003c/p\u003e \u003cp\u003e(on the basis of groundwater recharge demand)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.1-8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.1\u0026ndash;12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.1\u0026ndash;15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15\u0026lt;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003ePopulation Density\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;900\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eMore density, more suitability\u003c/p\u003e \u003cp\u003e(for demand purpose)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e901\u0026ndash;920\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e921\u0026ndash;950\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e951\u0026ndash;1000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1000\u0026lt;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eDistance from Agricultural land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eLess distance, more suitability\u003c/p\u003e \u003cp\u003e(for irrigation purposes)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e501\u0026ndash;1000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1001\u0026ndash;1500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1501\u0026ndash;2000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2000\u0026lt;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eDistance from Urban Area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;1000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eMore distance, more suitability\u003c/p\u003e \u003cp\u003e(less demand of urban people, difficult to make a structure in a built-up area)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1001\u0026ndash;2000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2001\u0026ndash;3000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3001\u0026ndash;4000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4000\u0026lt;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eDistance from Road\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;1500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eAverage distance, more suitability\u003c/p\u003e \u003cp\u003e(for transportation)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1501\u0026ndash;3000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3001\u0026ndash;4500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4501\u0026ndash;6000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6000\u0026lt;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eDistance from Stream\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;2000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eMore distance, more suitability\u003c/p\u003e \u003cp\u003e(already has water sources)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2001\u0026ndash;4000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4001\u0026ndash;6000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6001\u0026ndash;8000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8000\u0026lt;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe final output map was created based on RWHPI, which is a dimensionless indicator to classify the map into many layers according to the suitability condition of RWH potentiality of the study area. For this study area, the map was classified into three classes (poor, moderate, good) according to RWHPI based on the Jenks Natural Break method. This method is useful\u003c/p\u003e \u003cp\u003efor getting an optimum arrangement of values to convert into the suitability classes (Jenks, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e1967\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results and Discussion","content":"\u003cdiv id=\"Sec26\" class=\"Section2\"\u003e \u003ch2\u003e3.1. RWH potential zone map\u003c/h2\u003e \u003cp\u003eAll fourteen criteria (nine bio-physical and five socio-economic criteria) were integrated according to their reclassification to overlay the RWH potential zone map using ArcGIS.\u003c/p\u003e \u003cdiv id=\"Sec27\" class=\"Section3\"\u003e \u003ch2\u003e3.1.1. Rainfall map\u003c/h2\u003e \u003cp\u003eThe map shows that rainfall is increasing gradually towards the northern portion of the study area (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). The rainfall amount of the Rangpur division is much higher than that of the Rajshahi division. The highest value of rainfall was found at the northernmost part of the Rangpur division. 1501\u0026ndash;1800 mm rainfall covers 35% of the study area, which is the highest amount of area among all feature classes of rainfall. Lowest rainfall (less than 1500 mm) occurs in 2%, and the highest rainfall (greater than 2500 mm) occurs in 19% of the study area (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section3\"\u003e \u003ch2\u003e3.1.2. Slope map\u003c/h2\u003e \u003cp\u003eThis study area has mostly gentle slope (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb), which increases the chances of harvesting rainwater. The maximum portion (60%) of the study area has a 1.1 to 3 degree slope. The lowest amount of slope (less than 1 degree) covers 25% of the study area, and the steepest slope (greater than 10 degrees) covers only 1% of the study area (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec29\" class=\"Section3\"\u003e \u003ch2\u003e3.1.3. Soil type map\u003c/h2\u003e \u003cp\u003eThe study area is composed of four types of soil (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec). Most of the soil is loam, which covers 69% of the study area. Other types are clay, sandy clay loam, and sandy loam, which occupy respectively 11%, 4% and 14% of the study area. The remaining area (2%) is occupied by water (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec30\" class=\"Section3\"\u003e \u003ch2\u003e3.1.4. Drainage density map\u003c/h2\u003e \u003cp\u003e75% of the total area has a drainage density of 0.21\u0026ndash;0.40 km/km\u003csup\u003e2\u003c/sup\u003e, which indicates the study area has moderate drainage density. Least drainage density (less than 0.2 km/km\u003csup\u003e2\u003c/sup\u003e) covers 5% and the highest drainage density (more than 0.4 km/km\u003csup\u003e2\u003c/sup\u003e) covers 20% of the study area (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). The different ranges of drainage density are uniformly distributed through the Rangpur and the Rajshahi divisions (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTotal area and area percentages of every feature of all criteria used in RWH potential mapping\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCriteria\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFeature Class\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eArea (sq. km.)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eArea (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eRainfall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;1500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e471\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1501\u0026ndash;1800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e35.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1801\u0026ndash;2100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e29.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2101\u0026ndash;2500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2500\u0026lt;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6679\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eSlope\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8724\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.1-3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20816\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e60.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.1-5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4294\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.1\u0026ndash;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e615\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u0026lt;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eSoil Type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClay\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3788\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLoam\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23738\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e69.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSandy Clay Loam\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1483\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSandy Loam\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4835\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWater\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e687\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eDrainage Density\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1428\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.21\u0026ndash;0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e75.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.4\u0026lt;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eRunoff\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.1-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26674\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e77.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u0026lt;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4735\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003eLanduse Landcover\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWater\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1674\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFlooded Vegetation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCultivated Land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21981\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e63.66\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBuilt-up Area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBare Ground\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e883\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWetland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e591\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eEvapotranspiration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e301\u0026ndash;500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8727\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25.198\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e501\u0026ndash;700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23813\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e69.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e701\u0026ndash;900\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1485\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e900\u0026lt;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eDepression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31847\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e92.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.1-5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2419\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u0026lt;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e254\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eGroundwater Table\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5451\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.1-8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e55.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.1\u0026ndash;12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4992\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.1\u0026ndash;15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2695\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15\u0026lt;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2398\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003ePopulation Density\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;900\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6576\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e901\u0026ndash;920\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e921\u0026ndash;950\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e951\u0026ndash;1000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14.60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1000\u0026lt;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22767\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e66.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eDistance from Agricultural land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27341\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e79.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e501\u0026ndash;1000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5971\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1001\u0026ndash;1500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e502\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1501\u0026ndash;2000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e538\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2000\u0026lt;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eDistance from Urban Area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;1000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31447\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e91.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1001\u0026ndash;2000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2434\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2001\u0026ndash;3000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e473\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3001\u0026ndash;4000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e170\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4000\u0026lt;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eDistance from Road\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;1500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7968\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1501\u0026ndash;3000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4946\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3001\u0026ndash;4500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5816\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4501\u0026ndash;6000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3496\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6000\u0026lt;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12322\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e36.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eDistance from Stream\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;2000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27758\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e80.35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2001\u0026ndash;4000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6362\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4001\u0026ndash;6000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e375\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6001\u0026ndash;8000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8000\u0026lt;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec31\" class=\"Section3\"\u003e \u003ch2\u003e3.1.5. Runoff map\u003c/h2\u003e \u003cp\u003eThough the runoff amount is slightly higher in the Rangpur division (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea), mainly moderate runoff was generated throughout the whole study area. 9%, 77% and 14% of the study area show less than 1 m\u003csup\u003e3\u003c/sup\u003e/sec, 1.1-2 m\u003csup\u003e3\u003c/sup\u003e/sec, and more than 2 m\u003csup\u003e3\u003c/sup\u003e/sec runoff (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec32\" class=\"Section3\"\u003e \u003ch2\u003e3.1.6. Landuse landcover map\u003c/h2\u003e \u003cp\u003eThis study area is mainly agricultural (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb), having 63.66% of the total area, which is the highest among the seven types of landuse landcover. It indicates a great rainwater demand for farming activities. 21% area contains built-up area, 6.3% area contains forest and trees, 5% area contains water bodies, and a very small amount of areas (2.5%, 1.5% and 0.04%) have bare ground, wetland, and flooded vegetation, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec33\" class=\"Section3\"\u003e \u003ch2\u003e3.1.7. Evapotranspiration map\u003c/h2\u003e \u003cp\u003eA balanced scenario of evapotranspiration can be seen from the map (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec). The maximum (69%) area shows an evapotranspiration rate of 501\u0026ndash;700 mm/year. The lowest (less than 300 mm/year) and the highest (greater than 900 mm/year) evapotranspiration rate covers very little area, which are respectively 1.5% and 0.002% of the total area (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec34\" class=\"Section3\"\u003e \u003ch2\u003e3.1.8. Depression map\u003c/h2\u003e \u003cp\u003eThe depression map shows a very small depression in the soil surface (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed). About 92% of the study area has less than 2 m of depression (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e), which indicates that people can use depression as a natural storage for smaller purposes. Very little area of the Rajshahi division has slightly higher depression, whereas the Rangpur division has uniformly lower depression.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec35\" class=\"Section3\"\u003e \u003ch2\u003e3.1.9. Groundwater table map\u003c/h2\u003e \u003cp\u003eThe map presents a significantly deeper groundwater table in the Rajshahi division. On the other hand, the Rangpur division contains most of the shallow groundwater table (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea). Overall, the maximum part (55%) of the whole study area has groundwater table of 5.1-8 m. The shallowest and the deepest groundwater table (less than 5 m and greater than 15 m) covers 16% and 7% of the whole area, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec36\" class=\"Section3\"\u003e \u003ch2\u003e3.1.10. Population density map\u003c/h2\u003e \u003cp\u003eIt can be seen from the map that this study area is overpopulated (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb). The maximum portion (66%) of the study area occupies the highest population density of greater than 1000 person/km\u003csup\u003e2\u003c/sup\u003e. Less than 900 person/km\u003csup\u003e2\u003c/sup\u003e population density can be seen only in 19% of the study area (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec37\" class=\"Section3\"\u003e \u003ch2\u003e3.1.11. Distance from agricultural land map\u003c/h2\u003e \u003cp\u003eAs this study area is dominated by agricultural lands (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb), maximum areas are near any farm. The map illustrates, maximum areas (79%) are within 500 m of any agricultural land, and only 1% of the total area has a distance of 2000 m or more from any farm (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec38\" class=\"Section3\"\u003e \u003ch2\u003e3.1.12. Distance from urban area map\u003c/h2\u003e \u003cp\u003eAfter cultivated lands, this study area is dominated by urban areas (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb). Urban areas are more in the Rangpur division than in the Rajshahi division. Therefore, most of the total area (91%) is within the nearest distance (less than 1000 m) from any built-up areas (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec39\" class=\"Section3\"\u003e \u003ch2\u003e3.1.13. Distance from road map\u003c/h2\u003e \u003cp\u003eDistance from road map creates a relation between the transportation system with the RWH potential. A roughly balanced condition can be seen in the map according to the area percentage of different ranges (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ec). Most of the area (36%) has a distance of greater than 6000 m from the road network. Again, less than 1500 m distance is covered by 23% of the study area. Each moderate distance ranges also have from 10% to 17% of the total area (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec40\" class=\"Section3\"\u003e \u003ch2\u003e3.1.14. Distance from stream map\u003c/h2\u003e \u003cp\u003eThis map (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ed) represents the distance between each place and the closest stream. About 80.35% of the study area is within 2000 m of any stream. The highest distance (greater than 8000 m) is shown by only 0.05% of the total area (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec41\" class=\"Section3\"\u003e \u003ch2\u003e3.1.15. RWH potential map\u003c/h2\u003e \u003cp\u003eAll criteria layers were integrated and combined to calculate the RWH potential index (RWHPI). Total four RWH potential zone map were created from different normalized weight scenario after sensitivity analysis: (a) \u0026lsquo;base AHP\u0026rsquo; (scenario from normalized weight using pairwise comparison matrix), (b) \u0026lsquo;case \u003cspan refid=\"FPar1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u0026rsquo; (bio-physical criteria dominating scenario), (c) \u0026lsquo;case 2\u0026rsquo; (socio-economic criteria dominating scenario) and (d) \u0026lsquo;case 3\u0026rsquo; (equal importance to both bio-physical and socio-economic criteria). The map displays RWHPI values from 0.12 to 0.33, which were classified into three suitability classes: (a) \u0026lsquo;poor\u0026rsquo; (RWHPI ranges from 0.12 to 0.21), (b) \u0026lsquo;moderate\u0026rsquo; (RWHPI ranges from 0.22 to 0.24) and (c) \u0026lsquo;good\u0026rsquo; (RWHPI ranges from 0.25 to 0.33). In this classification, \u0026lsquo;poor\u0026rsquo; indicates unsuitable RWH zone, \u0026lsquo;moderate\u0026rsquo; and \u0026lsquo;good\u0026rsquo; indicate the possibility of RWH, where \u0026lsquo;good\u0026rsquo; presents the most suitable RWH zone.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec42\" class=\"Section3\"\u003e \u003ch2\u003e3.1.15.1. Base AHP\u003c/h2\u003e \u003cp\u003eIn this scenario, an area of 5784 km\u003csup\u003e2\u003c/sup\u003e (17%) is covered by the \u0026lsquo;poor\u0026rsquo; RWH potential zone. 18263 km\u003csup\u003e2\u003c/sup\u003e, which is the majority of the total area (54%) are in the \u0026lsquo;moderate\u0026rsquo; RWH potential zone, distributed uniformly all over the study area. An area of 9930 km\u003csup\u003e2\u003c/sup\u003e (29%) is covered by the \u0026lsquo;good\u0026rsquo; RWH potential zone. The northernmost region dominates with good RWH potential, and almost no poor RWH potential area in that region (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ea).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec43\" class=\"Section3\"\u003e \u003ch2\u003e3.1.15.2. Case \u003cspan refid=\"FPar1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003c/h2\u003e \u003cp\u003e \u003cstrong\u003eCase 1\u003c/strong\u003e \u003cp\u003eshows \u0026lsquo;moderate\u0026rsquo; RWH potential zone dominates (56%) the output map, covering an area of 19027 km\u003csup\u003e2\u003c/sup\u003e. The area percentage of \u0026lsquo;poor\u0026rsquo; and \u0026lsquo;good\u0026rsquo; RWH potential zones is close to each other, occupying an area of 6795 km\u003csup\u003e2\u003c/sup\u003e (20%) and 8155 km\u003csup\u003e2\u003c/sup\u003e (24%) of the study area, respectively. This map (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eb) displays an almost identical pattern to the output map of the scenario \u0026lsquo;base AHP\u0026rsquo; (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ea).\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec44\" class=\"Section3\"\u003e \u003ch2\u003e3.1.15.3. Case 2\u003c/h2\u003e \u003cp\u003e\u0026lsquo;Poor\u0026rsquo; RWH potential zone occupies a large portion (66%) of the map of case 2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ec), which is an area of 22425 km\u003csup\u003e2\u003c/sup\u003e. \u0026lsquo;Moderate\u0026rsquo; RWH potential zone covers an area of 10873 km\u003csup\u003e2\u003c/sup\u003e (32%) of the study area. Only 2% (679 km\u003csup\u003e2\u003c/sup\u003e) of the study area is covered by \u0026lsquo;good\u0026rsquo; RWH potential zone.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec45\" class=\"Section3\"\u003e \u003ch2\u003e3.1.15.4. Case 3\u003c/h2\u003e \u003cp\u003eIn this scenario (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ed), \u0026lsquo;poor\u0026rsquo;, \u0026lsquo;moderate\u0026rsquo;, and \u0026lsquo;good\u0026rsquo; RWH potential zone occupies an area of 13251 km\u003csup\u003e2\u003c/sup\u003e (39%), 18687 km\u003csup\u003e2\u003c/sup\u003e (55%), and 2039 km\u003csup\u003e2\u003c/sup\u003e (6%) of the total area, respectively. This map shows better RWH potential than case 2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ec) in the northernmost part.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec46\" class=\"Section3\"\u003e \u003ch2\u003e3.1.16. Sensitivity analysis through area comparison\u003c/h2\u003e \u003cp\u003eFrom the calculation of the area percentage of the four scenarios (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003ea), a significant change can be seen in each class (\u0026lsquo;poor\u0026rsquo;, \u0026lsquo;moderate\u0026rsquo;, and \u0026lsquo;good\u0026rsquo;) of each map. The general correlation (from \u0026lsquo;base AHP\u0026rsquo; scenario) of both types of criteria shows bio-physical criteria domination over socio-economic criteria, which is almost as same as case \u003cspan refid=\"FPar1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. For this reason, the area percentages of each class of \u0026lsquo;base AHP\u0026rsquo; and \u0026lsquo;case \u003cspan refid=\"FPar1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u0026rsquo; are almost similar. But in case 2 and case 3, a significant normalized weight change of both types of criteria occurred, which created an impact on the area percentage of the suitability class. In case 2 and case 3, the area of \u0026lsquo;poor\u0026rsquo; RWH potential zone has increased by 49% and 22% respectively from the \u0026lsquo;base AHP\u0026rsquo; scenario, 46% and 19% respectively from case \u003cspan refid=\"FPar1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. On the other hand, in case 2 and case 3, the area of \u0026lsquo;good\u0026rsquo; RWH potential zone has decreased by 27% and 23%, respectively, from the \u0026lsquo;base AHP\u0026rsquo; scenario, 22% and 18% respectively, from case \u003cspan refid=\"FPar1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Though from the \u0026lsquo;base AHP\u0026rsquo; scenario and case \u003cspan refid=\"FPar1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the area percentage of \u0026lsquo;moderate\u0026rsquo; RWH potential zone has reduced almost half in case 2, remains almost the same in case 3.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec47\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Validation\u003c/h2\u003e \u003cp\u003eWhen a new analytical approach is going to be implemented for publication or regular use, it is important to validate the output to verify the process or ensure the quality of the result. Without validation, the whole approach loses its acceptability (Chung and Fabbri 2003; Peters et al. 2007). Sometimes, an error can occur that might remain undetected while the study was conducted. For this reason, a proper method of validation is needed (Peters et al. 2007). In this study, the validation process was executed through accuracy assessment method.\u003c/p\u003e \u003cdiv id=\"Sec48\" class=\"Section3\"\u003e \u003ch2\u003e3.2.1. Accuracy assessment\u003c/h2\u003e \u003cp\u003eTo determine the optimal RWH potential map out of every scenario, an accuracy assessment was conducted. Accuracy assessment is necessary to verify the output from remotely sensed data to increase its reliability (Congalton \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e1991\u003c/span\u003e). Accuracy assessment is executed by choosing some random points in the generated map, comparing the point result from the map with the ground truth data (Aronoff \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1985\u003c/span\u003e). From the comparison of the map and ground truth data, an error or confusion matrix is built (Congalton \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e1991\u003c/span\u003e; Story and Congalton 1986). Finally, 4 accuracy indicators: (a) producer\u0026rsquo;s accuracy, (b) user\u0026rsquo;s accuracy, (c) overall accuracy, and (d) kappa accuracy are calculated to come to a final decision (Wu et al. 2018; Congalton \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e1991\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFor accuracy assessment, the known ground truth value is the most important thing, which needs to be collected accurately. In this study, RWH potential maps were generated using a new analytical approach according to the relative importance of fourteen criteria, which were calculated from a pairwise comparison. So, it is difficult to define ground truth values for a new RWH potential map, which was generated through a complex analysis of many criteria. But to validate the output maps and determine the optimal RWH potential map from four scenarios, some obvious known ground truth values were considered, as per opinions from experts. Firstly, 150 random accuracy assessment points for each scenario were created using ArcGIS (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e). Then, the points which were located exactly at the river, road, or dense urban area were classified as \u0026lsquo;poor\u0026rsquo; RWH potential zone for ground truth value because it is impossible to create an RWH structure in those types of locations. The points which were located at existing RWH structures like ponds, pans, and nala bunds were classified as \u0026lsquo;good\u0026rsquo; RWH potential zone for ground truth value. All ground truth points were evaluated using Google Earth Pro satellite imagery. Among 150 accuracy assessment points, only these points with obviously known values from the ground were considered for generating a confusion\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003ematrix (Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e), based on \u0026lsquo;poor\u0026rsquo; and \u0026lsquo;good\u0026rsquo; classes, which led to a limited but proper accuracy assessment with valid reasoning.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec49\" class=\"Section3\"\u003e \u003ch2\u003e3.2.2. Base AHP\u003c/h2\u003e \u003cp\u003eOut of 150 points, a total of 59 points satisfied the condition of being a known ground truth value in this scenario. After generating the confusion matrix, the resulting producer\u0026rsquo;s accuracy was 81%, the user\u0026rsquo;s accuracy was 83%, the overall accuracy was 80% and the kappa accuracy was 60% (Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec50\" class=\"Section3\"\u003e \u003ch2\u003e3.2.3. Case \u003cspan refid=\"FPar1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003c/h2\u003e \u003cp\u003eOf the 150 random points created for this scenario, only 46 points met the requirement of functioning as reference ground truth data. The accuracy parameter value was 81% for producer\u0026rsquo;s accuracy, 86% for user\u0026rsquo;s accuracy, 83% for overall accuracy, and 63% for kappa accuracy (Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGenerated confusion matrix for all scenario from the random point values of RWH potential map and ground truth values\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBase AHP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClass\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePoor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabb\" border=\"1\"\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCase \u003cspan refid=\"FPar1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClass\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePoor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabc\" border=\"1\"\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCase 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClass\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePoor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabd\" border=\"1\"\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCase 3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClass\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePoor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec51\" class=\"Section3\"\u003e \u003ch2\u003e3.2.4. Case 2\u003c/h2\u003e \u003cp\u003eA total of 58 points were considered as ground truth values in case 2. In this scenario, the resulting producer\u0026rsquo;s accuracy was 79%, the user\u0026rsquo;s accuracy was 97%, the overall accuracy was 95% and the kappa accuracy was 70% (Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec52\" class=\"Section3\"\u003e \u003ch2\u003e3.2.5. Case 3\u003c/h2\u003e \u003cp\u003eA total of 56 points among the random 150 accuracy assessment points were verified as the known ground truth value. In case 3, all 4 accuracy parameters showed 100% accuracy (Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e), which was determined from the confusion matrix (Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). Thus, this validation process indicates that case 3 is the optimal RWH potential scenario in this study. Figure\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e shows the comparison between all accuracy parameter for all scenario.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMain accuracy indicator of confusion matrix for all scenario\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScenario\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAccuracy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePoor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAll\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBase AHP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProducer's\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e96%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e81%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUser's\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e96%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e69%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e83%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e80%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKappa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e60%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCase \u003cspan refid=\"FPar1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProducer's\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e96%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e81%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUser's\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e93%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e78%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e86%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e83%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKappa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e63%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCase 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProducer's\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e80%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e90%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUser's\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e99%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e99%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e99%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKappa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e88%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCase 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProducer's\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e100%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUser's\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e100%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e100%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKappa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e100%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec53\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Discussion\u003c/h2\u003e \u003cdiv id=\"Sec54\" class=\"Section3\"\u003e \u003ch2\u003e3.3.1. Scenario Analysis\u003c/h2\u003e \u003cp\u003eThis study focuses on mapping suitable zones for RWH with geospatial techniques and MCDM approaches, incorporating both bio-physical and socio-economic criteria. A sensitive analysis was done after the weighted overlay analysis, which demonstrated that changing the weight of social-economic criteria caused a gradual change in the percentage of area of good, moderate, and poor zones. This analysis validates that the socio-economic criteria should have their own weightage or degree of influence, much like bio-physical factors. Some previous studies incorporated the analysis of socio-economic factors by the Boolean threshold method (Shadmehri Toosi et al. 2020; Al-Adamat et al. 2010; Al-Adamat et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Al-shabeeb \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). But in this study, the socio-economic criteria were given their weight through AHP to correlate\u003c/p\u003e \u003cp\u003ewith bio-physical criteria perfectly. To understand how each of the criteria influences the suitability for RWH, four scenarios were analyzed. The base AHP scenario map focused on the AHP-derived weightage from literature review and expert opinion, case \u003cspan refid=\"FPar1\" class=\"InternalRef\"\u003e1\u003c/span\u003e focused on the biophysical criteria, the socio-economic criteria dominated in case 2, and in case 3, both biophysical and socio-economic criteria were given equal importance. The changes in weightage (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e) had a huge impact on the suitability map. Scenario of the base AHP and case \u003cspan refid=\"FPar1\" class=\"InternalRef\"\u003e1\u003c/span\u003e showed almost relatively similar outcomes, which showed a larger extent of good and moderate suitability of RWH. On the other hand, since case 2 emphasized the socio-economic criteria, there was a drastic change in the percentage of area in good, moderate, and poor suitable zones compared to base AHP scenario and case \u003cspan refid=\"FPar1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Case 3, which assigned equal importance to both biophysical and socio-economic criteria, showed a more balanced and favorable result (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFurther, an accuracy assessment was done, and validation was done through analyzing the kappa coefficient(Al-Khuzaie et al. 2020) Here, the result of the validation showed that case 3, with a kappa coefficient of 1.0, is the most suitable scenario for RWH site selection, outperforming the other three scenarios, where base AHP scenario, case \u003cspan refid=\"FPar1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, and case 2 had a kappa coefficient of 0.60, 0.63, and 0.70, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e). The RWH map of case 3 showed that Rangpur division, having 11% of suitable sites for RWH, is more promising than Rajshahi division, which had only 2% of suitable zones for RWH in the north-west part of Bangladesh (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e). From the map, it was identified that the most promising suitable zones for RWH were in the Thakurgaon, Panchagarh, Kurigram, Lalmonirhat, and Nilphamari districts, along with some parts in Pabna district. In the previous study of (Rana and Moniruzzaman 2023), in the same study area, it was found that the districts Shirajganj, Kurigram, Gaibandha, Rangpur, Thakurgaon, Dinajpur, Panchagarh, and Nilphamari were mostly suitable for RWH structures. On the other hand, Pabna, Natore, Rajshahi, Nawabganj, Naogaon, Joypurhat, and Bogra are less ideal for RWH structures. There are certain differences in both studies; this may be due to the criteria selected for analysis and the time range of collected data that were used.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec55\" class=\"Section3\"\u003e \u003ch2\u003e3.3.2. Criteria analysis\u003c/h2\u003e \u003cp\u003eThe study explored the correlation-based regression analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e14\u003c/span\u003e) between different criteria and the RWH Potential Index (RWHPI) to understand the impact of each criterion(Jacob Cohen et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2003\u003c/span\u003e) in the final optimal output (RWH potential map of case 3). The R\u003csup\u003e2\u003c/sup\u003e value from the regression indicated the influence of bio-physical and socio-economic factors on the RWH potential map (Garc\u0026iacute;a and Caselles 1991; Al-Khuzaie et al. 2020).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAmong all the bio-physical criteria, rainfall (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003ea) and runoff (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003ed) showed the strongest correlation, R\u003csup\u003e2\u003c/sup\u003e of 0.209 and 0.2118, respectively, indicating that the suitability of RWH increases with higher rainfall and runoff. In socio-economic criteria, the distance from agricultural land (Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003ec) dominated the most by having the strongest correlation with RWHPI (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.302), with a negative slope indicating that the proximity to agricultural land increases the suitability for RWH. The other factors showed a moderate to low relation with RWHPI. Regarding landuse landcover (Fig.\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e14\u003c/span\u003eb), cultivated land showed the highest mean RWHPI (0.220). Areas with sandy loam soil have a higher mean RWHPI in the output map (0.224) (Fig.\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e14\u003c/span\u003ea). Although the study hypothesized that lower evaporation rate, an increase in population density, and lower proximity to urban areas increase the suitability of RWH, the regression analysis showed a contradictory result in these three criteria. These may have occurred because other criteria dominated the suitability score that the effect of these three factors were reduced and showed an opposite result. All the analysis demonstrate that in the northwest part of Bangladesh, most of the suitable site for RWH is located cultivation is high.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eBased on this insight, it can be concluded that on the northwest side of Bangladesh, the main focus for harvesting rainwater should be irrigation support for agriculture. Some structures, like ponds and pans, and percolation tanks, are suggested for RWH for irrigation purposes (Shadmehri Toosi et al. 2020).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"4. Conclusion","content":"\u003cp\u003eFor arid and semi-arid areas, RWH is a beneficial system as a solution to water scarcity on a large scale. This study explored the suitability of RWH in the water-scarce northwest region of Bangladesh, focusing on Rangpur and Rajshahi divisions. Using a Geographic Information System (GIS) integrated with the AHP, fourteen criteria, including nine bio-physical criteria and five socio-economic criteria, were given weight through the use of a pairwise comparison matrix. To determine the ideal normalized weight for both bio-physical and socio-economic criteria, four scenarios were generated through sensitivity analysis, from which the optimal potential zone for RWH was selected. This approach increased the reliability of the optimal site selection process.\u003c/p\u003e \u003cp\u003eAnalyzing fourteen criteria in a single pairwise comparison matrix is a complex process, but it shows the precise weight of each criterion based on its direct correlation with the others. From this direct correlation in this study, sensitivity analysis not only created different scenarios for RWH, but also showed that socio-economic criteria have a significant impact on creating RWH potential map. Among four scenarios, equal weight was given to bio-physical and socio-economic criteria (case 3) showed a better result in RWH potential mapping through validation. That optimal scenario illustrates that \u0026lsquo;poor\u0026rsquo;, \u0026lsquo;moderate\u0026rsquo;, and \u0026lsquo;good\u0026rsquo; RWH potential zone occupies an area of 13251 km\u003csup\u003e2\u003c/sup\u003e (39%), 18687 km\u003csup\u003e2\u003c/sup\u003e (55%), and 2039 km\u003csup\u003e2\u003c/sup\u003e (6%) of the study area, respectively. Among the sixteen districts in this study area, Thakurgaon, Panchagarh, Kurigram, Lalmonirhat, and Nilphamari districts, along with some parts of Pabna district, show comparatively better RWH suitability. Based on the comparison of divisions, the Rangpur division dominates in the good RWH potential zone. Finally, runoff and rainfall among all bio-physical criteria and distance from agricultural land among all socio-economic criteria show the highest impact through regression analysis in the final output.\u003c/p\u003e \u003cp\u003eThis optimal RWH potential zone map will help the planner to choose a suitable location for building an RWH structure. Most of the suitable RWH zones have agricultural land nearby, demonstrating the necessity of such RWH structures that will benefit farmers in irrigation, such as farm ponds, nala bunds, etc. Additionally, RWH can be practiced through various methods, such as percolation tanks and rooftop harvesting systems for domestic uses. These structures are cost-effective and easily manageable. Furthermore, areas with a lower groundwater table, like the Rajshahi division, can have recharge wells built there.\u003c/p\u003e \u003cp\u003eThe next phase of research should involve analyzing specific RWH structure potential under certain conditions for this area, estimating RWH potential under shifting climate patterns, and loss analysis from the storage system. Thus, this study\u0026rsquo;s efficient methodology could be valuable for addressing water scarcity in other regions, especially in regions with similar bio-physical and socio-environmental conditions.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that no funds, grants, or other support were received during the preparation of this manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that there are no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank the two anonymous reviewers for their constructive comments, which improved the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;\u003cstrong\u003eHera Dutta:\u003c/strong\u003e Conceptualization, Methodology, Data curation, Software, Formal Analysis, Validation, Writing - original draft.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;Seheba Yameen:\u003c/strong\u003e Conceptualization, Methodology, Data curation, Software, Formal Analysis, Validation, Writing - original draft.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;\u003cstrong\u003eMushfiqul Alam:\u003c/strong\u003e Conceptualization, Data curation, Software, Writing - original draft.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;Pollen Chakma:\u003c/strong\u003e Conceptualization, Data curation, Writing – review and editing.\u003c/p\u003e"},{"header":"References","content":"\u003cp\u003eAfsari, Navila, Sonia Binte Murshed, Sayed Mohammad Nazim Uddin, and Monzurul Hasan. 2022. “Opportunities and Barriers Against Successive Implementation of Rainwater Harvesting Options to Ensure Water Security in Southwestern Coastal Region of Bangladesh.” https://doi.org/10.3389/frwa.2022.811918.\u003c/p\u003e\n\u003cp\u003eAhmed, Sharif, Touhidul Islam, Abdul Haque, and Humnath Bhandari. 2023. “Effect of Rainfall Variability on Cropping Windows of Patuakhali and Rangpur Regions.”\u003c/p\u003e\n\u003cp\u003eAkter, Aysha, and Shoukat Ahmed. 2015a. “Potentiality of Rainwater Harvesting for an Urban Community in Bangladesh.” \u003cem\u003eJournal of Hydrology\u003c/em\u003e 528 (September):84–93. https://doi.org/10.1016/j.jhydrol.2015.06.017.\u003c/p\u003e\n\u003cp\u003eAl-Adamat, Rida, Saad AlAyyash, Hani Al-Amoush, Odeh Al-Meshan, Zahir Rawajfih, Akram Shdeifat, Adnan Al-Harahsheh, and Mohammed Al-Farajat. 2012. “The Combination of Indigenous Knowledge and Geo-Informatics for Water Harvesting Siting in the Jordanian Badia.” \u003cem\u003eJournal of Geographic Information System\u003c/em\u003e 04 (04): 366–76. https://doi.org/10.4236/jgis.2012.44042.\u003c/p\u003e\n\u003cp\u003eAl-Adamat, Rida, Abdullah Diabat, and Ghada Shatnawi. 2010a. “Combining GIS with Multicriteria Decision Making for Siting Water Harvesting Ponds in Northern Jordan.” \u003cem\u003eJournal of Arid Environments\u003c/em\u003e 74 (11): 1471–77. https://doi.org/10.1016/j.jaridenv.2010.07.001.\u003c/p\u003e\n\u003cp\u003eAli, Md. Hossain, Md. Hasanuzzaman, Md. Aktarul Islam, and Partha Biswas. 2022. “Groundwater Recharge Estimation at Barind Area, Bangladesh for Sustainable Groundwater Management: Application of Multiple Methods.” \u003cem\u003eEuropean Journal of Environment and Earth Sciences\u003c/em\u003e 3 (6): 23–29. https://doi.org/10.24018/ejgeo.2022.3.6.312.\u003c/p\u003e\n\u003cp\u003eAl-Khuzaie, Marwah M., Hussein Janna, and Nadhir Al-Ansari. 2020a. “Assessment Model of Water Harvesting and Storage Location Using GIS and Remote Sensing in Al-Qadisiyah, Iraq.” \u003cem\u003eArabian Journal of Geosciences\u003c/em\u003e 13 (21). https://doi.org/10.1007/s12517-020-06154-4.\u003c/p\u003e\n\u003cp\u003eAl-shabeeb, Abdel Rahman. 2016. “The Use of AHP within GIS in Selecting Potential Sites for Water Harvesting Sites in the Azraq Basin—Jordan.” \u003cem\u003eJournal of Geographic Information System\u003c/em\u003e 08 (01): 73–88. https://doi.org/10.4236/jgis.2016.81008.\u003c/p\u003e\n\u003cp\u003eAL-Shammari, Muthanna M.A., Ayser M. AL-Shamma’a, Ali Al Maliki, Hussain Musa Hussain, Zaher Mundher Yaseen, and Asaad M. Armanuos. 2021. “Integrated Water Harvesting and Aquifer Recharge Evaluation Methodology Based on Remote Sensing and Geographical Information System: Case Study in Iraq.” \u003cem\u003eNatural Resources Research\u003c/em\u003e 30 (3): 2119–43. https://doi.org/10.1007/s11053-021-09835-3.\u003c/p\u003e\n\u003cp\u003eAmin, M G Mostofa. 2005. “Rainwater Potential as a Supplemental Source for Dhaka City Supply.” https://www.researchgate.net/publication/258100609.\u003c/p\u003e\n\u003cp\u003eAmmar, Adham, Michel Riksen, Mohamed Ouessar, and Coen Ritsema. 2016. “Identification of Suitable Sites for Rainwater Harvesting Structures in Arid and Semi-Arid Regions: A Review.” \u003cem\u003eInternational Soil and Water Conservation Research\u003c/em\u003e. International Research and Training Center on Erosion and Sedimentation and China Water and Power Press. https://doi.org/10.1016/j.iswcr.2016.03.001.\u003c/p\u003e\n\u003cp\u003eAronoff, Stan. 1985. “The Minimum Accuracy Value as an Index of Classification Accuracy.”\u003c/p\u003e\n\u003cp\u003eBADC “Minor Irrigation Survey Report 2014–15. Bangladesh Agricultural Development Corporation (BADC), Ministry of Agriculture, Government of the People’s Republic of Bangladesh. 2015.” Accessed June 30, 2025. https://badc.portal.gov.bd/.\u003c/p\u003e\n\u003cp\u003eBalkhair, Khaled S., and Khalil Ur Rahman. 2021a. “Development and Assessment of Rainwater Harvesting Suitability Map Using Analytical Hierarchy Process, GIS and RS Techniques.” \u003cem\u003eGeocarto International\u003c/em\u003e 36 (4): 421–48. https://doi.org/10.1080/10106049.2019.1608591.\u003c/p\u003e\n\u003cp\u003eBangladesh National Portal. “Government Information and Services.” Accessed July 4, 2025. bangladesh.gov.bd.\u003c/p\u003e\n\u003cp\u003eBMD “Bangladesh Meteorological Department.” Accessed June 29, 2025. https://live6.bmd.gov.bd/.\u003c/p\u003e\n\u003cp\u003eBuraihi, Faez Hussein, Abdul Rashid, and Mohamed Shariff. 2015. “Selection of Rainwater Harvesting Sites by Using Remote Sensing and GIS Techniques: A Case Study of Kirkuk, Iraq.” Vol. 76. http://www.usgs.gov/.\u003c/p\u003e\n\u003cp\u003eBWDB. 2005. \u003cem\u003eRivers of Bangladesh\u003c/em\u003e. http://refhub.elsevier.com/S2589-7578(23)00022-7/rf0050.\u003c/p\u003e\n\u003cp\u003eCHIRPS “Climate Hazards Group InfraRed Precipitation with Station Data.” Accessed June 29, 2025. https://www.chc.ucsb.edu/data/chirps.\u003c/p\u003e\n\u003cp\u003eChung, Chang-Jo F, and Andrea G Fabbri. 2003. “Validation of Spatial Prediction Models for Landslide Hazard Mapping.” \u003cem\u003eNatural Hazards\u003c/em\u003e. Vol. 30.\u003c/p\u003e\n\u003cp\u003eCongalton, Russell G. 1991. “A Review of Assessing the Accuracy of Classifications of Remotely Sensed Data.” Vol. 37.\u003c/p\u003e\n\u003cp\u003eDarabi, Hamid, Ehsan Moradi, Ali Akbar Davudirad, Mohammad Ehteram, Artemi Cerda, and Ali Torabi Haghighi. 2021. “Efficient Rainwater Harvesting Planning Using Socio-Environmental Variables and Data-Driven Geospatial Techniques.” \u003cem\u003eJournal of Cleaner Production\u003c/em\u003e 311 (August). https://doi.org/10.1016/j.jclepro.2021.127706.\u003c/p\u003e\n\u003cp\u003eDas, Ashim C., Shihab A. Shahriar, Md A. Chowdhury, Md Lokman Hossain, Shahed Mahmud, Md Kamruzzaman Tusar, Romel Ahmed, and Mohammed Abdus Salam. 2023. “Assessment of Remote Sensing-Based Indices for Drought Monitoring in the North-Western Region of Bangladesh.” \u003cem\u003eHeliyon\u003c/em\u003e 9 (2). https://doi.org/10.1016/j.heliyon.2023.e13016.\u003c/p\u003e\n\u003cp\u003eDIVA-GIS “Data Interpolating Variational Analysis - Geographic Information System.” Accessed June 29, 2025. https://diva-gis.org/.\u003c/p\u003e\n\u003cp\u003eEsri Sentinel-2. “Esri | Sentinel-2 Land Cover Explorer.” Accessed June 29, 2025. https://livingatlas.arcgis.com/landcoverexplorer/#mapCenter=92.06716%2C26.51464%2C12.56\u0026amp;mode=step\u0026amp;timeExtent=2017%2C2024\u0026amp;year=2024.\u003c/p\u003e\n\u003cp\u003eFAO Soils Portal “Food and Agriculture Organization of the United Nations.” Accessed June 29, 2025. https://www.fao.org/soils-portal/data-hub/soil-maps-and-databases/faounesco-soil-map-of-the-world/en/.\u003c/p\u003e\n\u003cp\u003eFerozur, Rahaman Md, Chowdhury S. Jahan, Riad Arefin, and Quamrul H. Mazumder. 2019. “Groundwater Potentiality Study in Drought Prone Barind Tract, NW Bangladesh Using Remote Sensing and GIS.” \u003cem\u003eGroundwater for Sustainable Development\u003c/em\u003e 8 (April):205–15. https://doi.org/10.1016/j.gsd.2018.11.006.\u003c/p\u003e\n\u003cp\u003eFerozur Rahaman, Mohammad. 2017. “Morphometric Analysis of Major Watersheds in Barind Tract, Bangladesh: A Remote Sensing and GIS-Based Approach for Water Resource Management.” \u003cem\u003eHydrology\u003c/em\u003e 5 (6): 86. https://doi.org/10.11648/j.hyd.20170506.12.\u003c/p\u003e\n\u003cp\u003eGarcía, M. J.López, and V. Caselles. 1991. “Mapping Burns and Natural Reforestation Using Thematic Mapper Data.” \u003cem\u003eGeocarto International\u003c/em\u003e 6 (1): 31–37. https://doi.org/10.1080/10106049109354290.\u003c/p\u003e\n\u003cp\u003eGavhane, Kishor P., Anil Kumar Mishra, Arjamadutta Sarangi, Dhirendra Kumar Singh, and Susama Sudhishri. 2023. “Targeting of Rainwater Harvesting Structures Using Geospatial Tools and Analytical Hierarchy Process (AHP) in the Semi-Arid Region of Rajasthan (India).” \u003cem\u003eEnvironmental Science and Pollution Research\u003c/em\u003e 30 (22): 61682–709. https://doi.org/10.1007/s11356-023-26289-7.\u003c/p\u003e\n\u003cp\u003eGCN250 “Global Hydrologic Curve Number Dataset.” Accessed June 29, 2025. https://gee-community-catalog.org/projects/gcn250/.\u003c/p\u003e\n\u003cp\u003eGoel, A. K., and R. Kumar. 2005. “Economic Analysis of Water Harvesting in a Mountainous Watershed in India.” \u003cem\u003eAgricultural Water Management\u003c/em\u003e 71 (3): 257–66. https://doi.org/10.1016/j.agwat.2004.09.005.\u003c/p\u003e\n\u003cp\u003eHasan, Md Rashidul, Md Nuruzzaman, and Abdullah Al Mamun. 2019. “Contribution of Rainwater to the Irrigation Requirement for Paddy Cultivation at Tanore Upazila in Rajshahi, Bangladesh.” \u003cem\u003eAir, Soil and Water Research\u003c/em\u003e 12 (April). https://doi.org/10.1177/1178622119837544.\u003c/p\u003e\n\u003cp\u003eHasan, Md Towhidul, Chowdhury Sarwar Jahan, Md Ferozur Rahaman, and Quamrul Hasan Mazumder. 2022. “Delineation of Zones and Sites for Artificial Recharge of Groundwater in Dry Land Barind Tract, Bangladesh Using MCDM Technique in GIS Environment.” \u003cem\u003eSustainable Water Resources Management\u003c/em\u003e 8 (5). https://doi.org/10.1007/s40899-022-00713-1.\u003c/p\u003e\n\u003cp\u003eHassan, Waqed H., Karrar Mahdi, and Zahraa K. Kadhim. 2025. “Optimal Rainwater Harvesting Locations for Arid and Semi-Arid Regions by Using MCDM-Based GIS Techniques.” \u003cem\u003eHeliyon\u003c/em\u003e 11 (3). https://doi.org/10.1016/j.heliyon.2025.e42090.\u003c/p\u003e\n\u003cp\u003eHoque, Sonia F., and Robert Hope. 2020. “Examining the Economics of Affordability through Water Diaries in Coastal Bangladesh.” \u003cem\u003eWater Economics and Policy\u003c/em\u003e 6 (3). https://doi.org/10.1142/S2382624X19500115.\u003c/p\u003e\n\u003cp\u003eHuq, F F. 2020. “Impact of Groundwater Drought on Domestic Water Use in Barind Tract, Bangladesh.” \u003cem\u003eJ. Wat. Env. Sci\u003c/em\u003e 4:589–602. http://revues.imist.ma/?journal=jwes.\u003c/p\u003e\n\u003cp\u003eHydroSHEDS. n.d. “The Hydrological Data and Maps Based on SHuttle Elevation Derivatives at Multiple Scales.” Accessed June 29, 2025. https://www.hydrosheds.org/.\u003c/p\u003e\n\u003cp\u003eIslam, Riffat, and Md Kamruzzaman. 2015. “A Study on Urban Mobility in Northern Cities of Bangladesh.”\u003c/p\u003e\n\u003cp\u003eJacob Cohen, Patricia Cohen, Stephen G. West, and Leona S. Aiken. 2003. “Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences Third Edition.”\u003c/p\u003e\n\u003cp\u003eJenks, G.F. n.d. “Jenks, G.F., 1967. The Data Model Concept in Statistical Mapping. Int. Yearbook Cartography 7, 186–190.”\u003c/p\u003e\n\u003cp\u003eJha, Madan K., V. M. Chowdary, Y. Kulkarni, and B. C. Mal. 2014a. “Rainwater Harvesting Planning Using Geospatial Techniques and Multicriteria Decision Analysis.” \u003cem\u003eResources, Conservation and Recycling\u003c/em\u003e 83 (February):96–111. https://doi.org/10.1016/j.resconrec.2013.12.003.\u003c/p\u003e\n\u003cp\u003eKarimi, Hamid, and Hossein Zeinivand. 2021. “Integrating Runoff Map of a Spatially Distributed Model and Thematic Layers for Identifying Potential Rainwater Harvesting Suitability Sites Using GIS Techniques.” \u003cem\u003eGeocarto International\u003c/em\u003e 36 (3): 320–39. https://doi.org/10.1080/10106049.2019.1608590.\u003c/p\u003e\n\u003cp\u003eKränzlin, Irène. 2000. “Pond Management in Rural Bangladesh: Problems and Possibilities in the Context of the Water Supply Crisis.” \u003cem\u003eNatural Resources Forum\u003c/em\u003e 24 (3): 211–23. https://doi.org/10.1111/j.1477-8947.2000.tb00945.x.\u003c/p\u003e\n\u003cp\u003eMahmood, Khalid, Ansab Qaiser, Sumar Farooq, and Mehr un Nisa. 2020. “RS- and GIS-Based Modeling for Optimum Site Selection in Rain Water Harvesting System: An SCS-CN Approach.” \u003cem\u003eActa Geophysica\u003c/em\u003e 68 (4): 1175–85. https://doi.org/10.1007/s11600-020-00460-x.\u003c/p\u003e\n\u003cp\u003eMahmoud, Shereif H., J. Adamowski, A. A. Alazba, and A. M. El-Gindy. 2016. “Rainwater Harvesting for the Management of Agricultural Droughts in Arid and Semi-Arid Regions.” \u003cem\u003ePaddy and Water Environment\u003c/em\u003e 14 (1): 231–46. https://doi.org/10.1007/s10333-015-0493-z.\u003c/p\u003e\n\u003cp\u003eMatomela, Nametso, Tianxin Li, and Harrison O. Ikhumhen. 2020a. “Siting of Rainwater Harvesting Potential Sites in Arid or Semi-Arid Watersheds Using GIS-Based Techniques.” \u003cem\u003eEnvironmental Processes\u003c/em\u003e 7 (2): 631–52. https://doi.org/10.1007/s40710-020-00434-7.\u003c/p\u003e\n\u003cp\u003eMbilinyi, B. P., S. D. Tumbo, H. F. Mahoo, and F. O. Mkiramwinyi. 2007a. “GIS-Based Decision Support System for Identifying Potential Sites for Rainwater Harvesting.” \u003cem\u003ePhysics and Chemistry of the Earth\u003c/em\u003e 32 (15–18): 1074–81. https://doi.org/10.1016/j.pce.2007.07.014.\u003c/p\u003e\n\u003cp\u003eNASA MODIS “NASA Moderate Resolution Imaging Spectroradiometer.” Accessed June 29, 2025. https://modis.gsfc.nasa.gov/.\u003c/p\u003e\n\u003cp\u003eNowreen, Sara, Imran Hossain Newton, Rashed Uz Zzaman, A. K.M.Saiful Islam, G. M.Tarekul Islam, and Md Saiful Alam. 2021. “Development of Potential Map for Groundwater Abstraction in the Northwest Region of Bangladesh Using RS-GIS-Based Weighted Overlay Analysis and Water-Table-Fluctuation Technique.” \u003cem\u003eEnvironmental Monitoring and Assessment\u003c/em\u003e 193 (1). https://doi.org/10.1007/s10661-020-08790-5.\u003c/p\u003e\n\u003cp\u003eNyirenda, F., A. Mhizha, W. Gumindoga, and A. Shumba. 2021. “A Gis-Based Approach for Identifying Suitable Sites for Rainwater Harvesting Technologies in Kasungu District, Malawi.” \u003cem\u003eWater SA\u003c/em\u003e 47 (3): 347–55. https://doi.org/10.17159/wsa/2021.v47.i3.11863.\u003c/p\u003e\n\u003cp\u003eOh, Hyun Joo, Seung Chan Ahn, Jong Kuk Choi, and Saro Lee. 2011. “Sensitivity Analysis for the GIS-Based Mapping of the Ground Subsidence Hazard near Abandoned Underground Coal Mines.” \u003cem\u003eEnvironmental Earth Sciences\u003c/em\u003e 64 (2): 347–58. https://doi.org/10.1007/s12665-010-0855-1.\u003c/p\u003e\n\u003cp\u003ePeters, Frank T., Olaf H. Drummer, and Frank Musshoff. 2007a. “Validation of New Methods.” \u003cem\u003eForensic Science International\u003c/em\u003e 165 (2–3): 216–24. https://doi.org/10.1016/j.forsciint.2006.05.021.\u003c/p\u003e\n\u003cp\u003ePopulation \u0026amp; Housing Census Preliminary Report. 2022. “Bangladesh Bureau of Statistics and Informatics Division of Planning.” https://bbs.portal.gov.bd/.\u003c/p\u003e\n\u003cp\u003eQays Hashim, Hadeel, and Khamis Naba Sayl. 2020. “Detection of Suitable Sites for Rainwater Harvesting Planning in an Arid Region Using Geographic Information System.” https://doi.org/10.1007/s12518-020-00342-3/Published.\u003c/p\u003e\n\u003cp\u003eRahman, A T M Sakiur, Chowdhury Sarwar Jahan, Quamrul Hasan Mazumder, and Takahiro Hosono. 2017. “Drought Analysis and Its Implication in Sustainable Water Resource Management in Barind Area, Bangladesh.” \u003cem\u003eJournal Geological Society of India\u003c/em\u003e. Vol. 89.\u003c/p\u003e\n\u003cp\u003eRamakrishnan, D, and K N Kusuma. 2009. “SCS-CN and GIS-Based Approach for Identifying Potential Water Harvesting Sites in the Kali Watershed, Mahi River Basin, India.”\u003c/p\u003e\n\u003cp\u003eRana, M. M.Shah Porun, and Md Moniruzzaman. 2023a. “A Combined GIS, Remote Sensing and MCDM Approach to Find Potential Location for Rainwater Harvesting Structure in Northwestern Part of Bangladesh.” \u003cem\u003eHydroResearch\u003c/em\u003e 6 (January):235–46. https://doi.org/10.1016/j.hydres.2023.08.001.\u003c/p\u003e\n\u003cp\u003eRukunujjaman, Md. 2016. “Poverty Level of Bangladesh: Special Case, Rangpur and Rajshahi Division.” Vol. 5. www.ijsrm.humanjournals.comwww.ijsrm.humanjournals.com.\u003c/p\u003e\n\u003cp\u003eSaaty, T.L., 1980. The Analytic Hierarchy Process. McGraw-Hill, New York..\u003c/p\u003e\n\u003cp\u003eSaaty, Thomas L., and Kevin P. Kearns. 1985. “The Analytic Hierarchy Process.” In \u003cem\u003eAnalytical Planning\u003c/em\u003e, 19–62. Elsevier. https://doi.org/10.1016/b978-0-08-032599-6.50008-8.\u003c/p\u003e\n\u003cp\u003eSaha, Abir, Salahuddin Setu, Swadhin Das, Md Imran Hossain, AHM Khalequr Rahman, and Md Mafizur Rahman. 2024. “Decision Support System for Community Managed Rainwater Harvesting: A Case Study in the Salinity-Prone Coastal Region of Bangladesh.” \u003cem\u003eHeliyon\u003c/em\u003e 10 (9). https://doi.org/10.1016/j.heliyon.2024.e30455.\u003c/p\u003e\n\u003cp\u003eSayl, K. N., A. S. Mohammed, and A. D. Ahmed. 2020. “GIS-Based Approach for Rainwater Harvesting Site Selection.” In \u003cem\u003eIOP Conference Series: Materials Science and Engineering\u003c/em\u003e. Vol. 737. Institute of Physics Publishing. https://doi.org/10.1088/1757-899X/737/1/012246.\u003c/p\u003e\n\u003cp\u003eShadmehri Toosi, Amirhossein, Erfan Ghasemi Tousi, Seyed Ali Ghassemi, Ali Cheshomi, and Sina Alaghmand. 2020a. “A Multi-Criteria Decision Analysis Approach towards Efficient Rainwater Harvesting.” \u003cem\u003eJournal of Hydrology\u003c/em\u003e 582 (March). https://doi.org/10.1016/j.jhydrol.2019.124501.\u003c/p\u003e\n\u003cp\u003eShahid, Shamsuddin, and Manzul Kumar Hazarika. 2010. “Groundwater Drought in the Northwestern Districts of Bangladesh.” \u003cem\u003eWater Resources Management\u003c/em\u003e 24 (10): 1989–2006. https://doi.org/10.1007/s11269-009-9534-y.\u003c/p\u003e\n\u003cp\u003eShamsuzzoha, M, A Parvez, and A F M K Chowdhury. 2014. “Analysis of Variability in Rainfall Patterns in Greater Rajshahi Division Using GIS.” \u003cem\u003eJ. Environ. Sci. \u0026amp; Natural Resources\u003c/em\u003e. Vol. 7.\u003c/p\u003e\n\u003cp\u003eStory, Michael, and Russell G Congalton. “Accuracy Assessment: A User’s Perspective.”\u003c/p\u003e\n\u003cp\u003eSubramanya, K. 2008. \u003cem\u003eEngineering Hydrology\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003eTariqul Islam, Md, Md Mohabbat Ullah, M. G. Mostofa Amin, and Sahadat Hossain. 2017. “Rainwater Harvesting Potential for Farming System Development in a Hilly Watershed of Bangladesh.” \u003cem\u003eApplied Water Science\u003c/em\u003e 7 (5): 2523–32. https://doi.org/10.1007/s13201-016-0444-x.\u003c/p\u003e\n\u003cp\u003eTiwari, Kuldeep, Rohit Goyal, and Archana Sarkar. 2018. “GIS-Based Methodology for Identification of Suitable Locations for Rainwater Harvesting Structures.” \u003cem\u003eWater Resources Management\u003c/em\u003e 32 (5): 1811–25. https://doi.org/10.1007/s11269-018-1905-9.\u003c/p\u003e\n\u003cp\u003eTract, Barind, Bangladesh Bazlar Rashid, Badrul Islam, Md Bazlar Rashid, and Bazlar Rashid. 2014. “Bazlar Rashid, Sultan-Ul-Islam, Badrul Islam. Drainage Characteristics and Evolution of the Barind Tract.” \u003cem\u003eBangladesh. American Journal of Earth Sciences\u003c/em\u003e. Vol. 1. http://www.openscienceonline.com/journal/ajes.\u003c/p\u003e\n\u003cp\u003eUSGS Earth Explorer “United States Geological Survey Earth Explorer.” Accessed June 29, 2025. https://www.usgs.gov/tools/earthexplorer.\u003c/p\u003e\n\u003cp\u003eVictor Ponce, By M, and Richard H Hawkins. 1996. “Runoff Curve Number: Has it Reached Maturity?”\u003c/p\u003e\n\u003cp\u003eWard, S., F. A. Memon, and D. Butler. 2012. “Performance of a Large Building Rainwater Harvesting System.” \u003cem\u003eWater Research\u003c/em\u003e 46 (16): 5127–34. https://doi.org/10.1016/j.watres.2012.06.043.\u003c/p\u003e\n\u003cp\u003eWorldpop. “Open Spatial Demographic Data and Research.” Accessed June 29, 2025. https://www.worldpop.org/.\u003c/p\u003e\n\u003cp\u003eWu, Ray Shyan, Gabriela Lucia Letona Molina, and Fiaz Hussain. 2018a. “Optimal Sites Identification for Rainwater Harvesting in Northeastern Guatemala by Analytical Hierarchy Process.” \u003cem\u003eWater Resources Management\u003c/em\u003e 32 (12): 4139–53. https://doi.org/10.1007/s11269-018-2050-1.\u003c/p\u003e\n\u003cp\u003eZlaugotne, Beate, Lauma Zihare, Lauma Balode, Antra Kalnbalkite, Aset Khabdullin, and Dagnija Blumberga. 2020. “Multi-Criteria Decision Analysis Methods Comparison.” \u003cem\u003eEnvironmental and Climate Technologies\u003c/em\u003e 24 (1): 454–71. https://doi.org/10.2478/rtuect-2020-0028.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"water-resources-management","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"warm","sideBox":"Learn more about [Water Resources Management](https://www.springer.com/journal/11269)","snPcode":"11269","submissionUrl":"https://submission.nature.com/new-submission/11269/3","title":"Water Resources Management","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Rainwater harvesting, Multi-criteria decision making (MCDM), Analytic hierarchy process (AHP), Geographic Information System (GIS), Remote sensing","lastPublishedDoi":"10.21203/rs.3.rs-8449542/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8449542/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWater scarcity is a critical issue in the northwestern part of Bangladesh, particularly across the Rangpur and the Rajshahi divisions. Rainwater harvesting (RWH) presents a low-cost and sustainable solution to address this challenge by increasing local water availability. This study determines potential sites for RWH implementation using Geographic Information System (GIS) integrated with the Analytic Hierarchy Process (AHP), a widely used Multi-Criteria Decision Making (MCDM) approach. Fourteen criteria, including nine bio-physical criteria and five socio-economic criteria, were chosen to evaluate RWH sites. The correlation of bio-physical and socio-economic criteria was analyzed through a single pairwise comparison matrix. This globally applicable methodology was executed in the northwestern part of Bangladesh, including Rangpur and Rajshahi divisions. A sensitivity analysis was conducted under four different weighting scenarios to demonstrate diversity in decision-making judgements. To select the optimal scenario, validation was performed through an accuracy assessment method, where kappa accuracy was calculated 60% for scenario 1, 63% for scenario 2, 70% for scenario 3, and 100% for scenario 4. Based on kappa accuracy, scenario 4 is the optimal scenario for future RWH initiatives in the study area, which shows 39%, 55%, and 6% of the total area as poor, moderate, and good RWH potential zones. The final output showed that Thakurgaon, Panchagarh, Kurigram, Lalmonirhat, Nilphamari districts, along with some parts of Pabna district, were the most suitable for harvesting rainwater.\u003c/p\u003e","manuscriptTitle":"Optimal site selection for rainwater harvesting through a combined approach of GIS and MCDM","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-23 10:13:02","doi":"10.21203/rs.3.rs-8449542/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2026-02-23T14:02:36+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-21T12:51:53+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"Water Resources Management","date":"2026-01-09T14:22:53+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-26T13:29:53+00:00","index":"","fulltext":""},{"type":"submitted","content":"Water Resources Management","date":"2025-12-25T09:12:48+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"water-resources-management","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"warm","sideBox":"Learn more about [Water Resources Management](https://www.springer.com/journal/11269)","snPcode":"11269","submissionUrl":"https://submission.nature.com/new-submission/11269/3","title":"Water Resources Management","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"800a29be-25ff-4d3d-ab15-8090a70dd25e","owner":[],"postedDate":"January 23rd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-01-23T10:13:03+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-23 10:13:02","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8449542","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8449542","identity":"rs-8449542","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}