Modelling and Mapping of Artificial Groundwater Recharge Zones using Geospatial Techniques and Analytical Hierarchical Process (AHP) of Mochemad River Basin, Sindhudurg district, Maharashtra, India

Modelling and Mapping of Artificial Groundwater Recharge Zones using Geospatial Techniques and Analytical Hierarchical Process (AHP) of Mochemad River Basin, Sindhudurg district, Maharashtra, India | 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 Modelling and Mapping of Artificial Groundwater Recharge Zones using Geospatial Techniques and Analytical Hierarchical Process (AHP) of Mochemad River Basin, Sindhudurg district, Maharashtra, India Tejas S. Naik, Satyajit K. Gaikwad, Ajaykumar K. Kadam, Vasant M Wagh, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6331621/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The groundwater extraction in coastal area is increased in recent year due to ecotourism and climate change, this results into depletion of groundwater level which needs the modelling and mapping for artificial groundwater recharge zones (AGRZ). The present study carried out at Mochemad River basin at western coastal part of Maharashtra India. The study area is having sea water intrusion as major problem can be resolved by the reaching runoff water as area receive more 3500mm rainfall. In view of this, the present study uses thematic layer for instance Lithology, Geomorphology, Land utilisation, Gradient, Drainage and Lineament Density and Rainfall with multicritical based Analytical Hierarchical Process analysis for giving accurate weights to each geospatial layer and its sub class. Further, the modelling of AGRZ was assessed with weighted overlay analysis in geospatial software. The finalised map of AGRZ is classified into four categories namely, unsuitable (14.24%), moderately suitable (19.25%), highly suitable (55.94%) and very highly suitable (10.56%). Finally, findings were validated using an assessment matrix of ROC (receiver operating characteristics) and AUC (area under curve), which revealed that the AHP approach performed reliably with an accuracy of 89%. Furthermore, various artificial recharge constructions like check dams, runoff infiltration tanks, Mati Bandara (soil bench trenching), and continuous contour trenching (CCT), are proposed in the demarcated favourable zones to facilitate the development, forecasting, and management of the water resources in the study area. Analytical Hierarchical Process Artificial Groundwater Recharge Zones Geospatial Techniques Weighted Overlay Analysis 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 Introduction Groundwater is the utmost essential and trustworthy water reserve used for drinking and irrigation as well as for improvement of the socio-economic structure of any region (Kumari and Singh, 2021 , Howlader et al., 2024 ; Yazdi et al., 2024 ). Groundwater constitutes about 95% of total freshwater available on the earth (Freeze and Cherry, 1979). The worldwide problem of availability of fresh groundwater resource is of major concern. The disparity between finite groundwater resources and increasing its exploitation making the condition critical/worst (Kumari and Singh, 2021 ; Sahu et al., 2022 ). In coastal areas, due to over- exploitation of groundwater leading to drinking water shortage as well as intrusion of seawater (Bhattacharya, 2010, Rajasekhar et al., 2020 ). Rise and fall of water level on decadal scale showing stressful groundwater scenario in the southern Konkan, of which study area is a part (Das, 2022 ,). Increasing sea level will be affecting coastal aquifers badly (Gaikwad et al, 2020 ). Hence it is very much essential to understand various ways to recharge groundwater to improve its level for sustainable livelihood (Pitchaimani et al., 2024 ) Artificial recharge of groundwater by way of recharging structures is one of the efficient methods for groundwater management (Kaliraj, 2013). Therefore, it is important to augment the subsurface water possible by man-made recharge using geospatial techniques (Kumari and Singh, 2021 ). Many qualitative and quantitative techniques like Multi-Influencing Factor, Analytical Hierarchy Process (AHP) AHP, Boolean logic, Fuzzy logic and WetSpa was utilized for identifying artificial groundwater recharge zones (Saaty, 1980; Zaidi et. Al. 2015; Patel et al, 2022). The present is an initial attempt, for artificial groundwater recharge zones (AGRZ) using geospatial modelling and mapping using Multicriteria based AHP technique. The AHP prepares a pursue problem into a order and used for solving many complex problems related to decision making (Saaty 1988; Pishyar et al. 2020; Kumari and Singh, 2021 ; Patel et al, 2022). Recently many studies for the identification of groundwater potential zones have been carried out using GIS and remote sensing (Kolekar et al., 2017; Murmur et al., 2019; Dar et al., 2020; Kumari and Singh, 2021 ). A map classifying low, moderate and high groundwater potential zones is generated using various parameters and their assigned weightages (Kadam et al, 2023 ). Rainwater collection sites were found for groundwater recharge, allowing for better development, planning, and management of water resources. In the area under investigation no study has been carried out and/or reported for the identification of artificial water conservation sites using AHP techniques. This work is the first to undertake a longitudinal analysis in the Hodawada river basin. This paper investigates the usefulness of the GIS and AHP techniques in demarcation of artificial recharge structure in geologically varied lithologies. This research seeks to address the solution to the problem of groundwater availability and scarcity observed after February in varied geological formations in the Mochemad river basin. A holistic approach is utilized integrating various parameters with their weightages for AHP analysis and for locating groundwater recharge structures. The techniques used here can be utilized in the area having similar geology and geomorphology especially in the coastal parts. Study Area The area under investigation is the Mochemad River Basin present in the Sindhudurg district, southerly part of the Konkan area, west coast of India. The basin is spread in Kudal, Sawantwadi and Vengurla Tehsils of Sindhudurg district. It is bounded by Longitudes 73 0 39’ to 73 o 49’ E to Latitudes 15 o 47’ N to 15o 57’ N and is included in the Survey of India’s Toposheet no. 48 E/9 and 48 E/13 of scale 1:50,000. The river Mochemad originates in the hills of Humras village (elevation of 131 mts) near Kudal and flows SW through Vengurla tehsil and meets Arabian Sea near Tak village (Fig. 1 ). The Mochemad River has the total length of 28 km and basin area of about 130 km2. The rainfall ranges between 3000 and 4700 mm/year (Bandaru et al, 2016 ). The basin receives around 3070 mm of rain on average (CGWB, 2014). According to physiography, the eastern portion of the river is made up of flat-topped hills with a Laterite-covered undulating plateau, while the western portion is the coastal plain (CGWB, 2014). Laterites, granites, and granitic gneisses are the main aquifer formations. The groundwater yields between 2 and 5 m 3 /day and is found under an unconfined aquifer at moderately shallow depths ranging from 2 to 10 m bgl. Both bore and dug wells typically have a depth of 2 to 11 meters and a yield of 2 to 5 meters per day in coastal alluvium. While the bore wells are 50 to 70 meters deeper and have yields ranging from 500 to 7770 litres per hour, the wells in the Gneissic complex are 3 to 11 meters deep and have a yield of 2 to 3 m3/day. The laterite found in the basin's northeast and south can have specific capacities ranging from 79.10 to 424.57 litres per minute of drawdown, transmissivities between 46.59 and 375.22 m 2 /day, and permeabilities between 7.40 to 425.22 m/day (CGWB, 2014). Method and Material The multi-parametric analysis for demarcating artificial groundwater recharge areas of Mochemad watershed has been done using AHP technique in GIS environment. The current study is implemented in the following methods (Fig. 2 ). In the study of the Mochemad River Basin, seven criterions such as Geology (GG), Geomorphology (GM), Drainage Density (DD), Slope (S), Lineament density (LD), Soil type and LULC have been analysed by AHP approach using normalized weight to demarcate area for artificial groundwater recharge. Seven spatial criterions like Geology (GG), Geomorphology (GM), Drainage Density (DD), Slope (S), Lineament density (LD), Soil type and LULC have been used for the preparation of geospatial database. Lithological and geomorphological map was prepared using district resource map of Sindhudurg district. The Survey of India (SOI) toposheet, from which the drainage density layer was created, was used to digitise the stream network. To create a slope map, SRTM DEM was taken from US Geological Survey Earth Explorer and processed. The "Manual for Geomorphology and Lineament mapping (web version)" was used to digitise the lineament map, which was then processed to determine its density. LISS 3 satellite imagery was used to create the Land Use and Land Cover (LULC) map, and the National Bureau of Soil Survey and Land Use Planning (NBSS and LUP, Nagpur) map was used to create the soil type map layer (Reshmidevi et al., 2008 ). Table.1: Details of the data used for the study their source Data Type Sources Data used for Topographical maps No. 48 E/9 and 48 E/13 (Scale 1:50,000) Survey of India (SOI) Drainage map GEDM (Resolution = 30m) Glovis Relief, slope, drainage, topographic Geological map GSI District Resource Map, Geology Geomorphological map BHUVAN Geomorphology LULC Data LISS- III satellite imagery LULC Map Soil data National Bureau of Soil Survey and land Use Planning Soil type Lineament Data Toposheet map and GSI map Lineament density and map. Groundwater fluctuation data Field data Groundwater level Multi- criteria Decision making using Analytical Hierarchical process (AHP) In this study of Mochemad River Basin, AHP is utilized to demarcate the regions for artificial groundwater recharge. The method was suggested by Saaty (1980) for solving complicated decision-making issues. Field study and experts view was used to assign the weight of the Saaty’s 1–9 scale for each thematic layer. The response of these influencing parameters is weighted as per their reaction to recharge of groundwater. A factor with high rank is the layer with high impact and factor with low rank are with low impact on the groundwater recharge. AHP was used for assigning the weights and calculate the normalized weights for the parameters influencing groundwater recharge. The Pairwise Comparison Matrix of thematic layers such as Geology, Geomorphology, Slope, Drainage Density, Soil Type, Lineament Density and LULC were compared according to the 1–9 scale suggested by Saaty (2008). The consistency ratio (CR) and consistency index (CI) values were computed to examine the reliability of the obtained results. The formula used is CR \(\:=\frac{\text{C}\text{I}}{\text{R}\text{I}}\) Where CR is consistency ratio, RI is random consistency index whose values are derived from the order of matrix. CI is consistency index which is calculated from the formula given below CI \(\:=\frac{\:{\lambda\:}-\text{n}}{\text{n}-1}\) Where λ is the principal Eigen value of the matrix and n is the number of parameters affecting groundwater recharge. The CR should be less than 0.1 to avoid the inconsistency. Result and Discussion Geology: The porosity and permeability of rocks determine the presence and flow of groundwater (Balaji et al. 2019; Luo et al, 2020; Khan et al, 2022 ). There is a variety of lithology in the research region (Deendar, 2003 ). Eleven distinct geological units, ranging in age from Pre-Cambrian to modern, make up the river basin under study (Gaikwad et al., 2020 ). A suite of Tonalite, Trondjhemite, and Granodiorite (TTG) gneisses are mostly exposed, together with granitoids and migmatites that contain enclaves of Banded Iron Formation (BIF), Amphibolite, and Ultramafite. Dolerite dykes and quartz veins penetrate the biotite TTG, which is the basement for the supracrustal rocks in the region, such as Metapellite and BIF. Laterite is developed over the country rock at higher elevations and fluvio- marine sediments are present near the mouth of the river (GSI, 2012). The homogeneously foliated Biotite-TTG gneiss and fluvio-marine deposits demonstrates high infiltration capacity and hence given highest rank (Abijith et al. 2020; Mahato et al, 2022). Both primary and secondary laterite are found in large quantities in low-lying platens and along the Western coast. They are porous, although their impermeability will limit infiltration. As a result, it has the lowest ranking. Geomorphology Geomorphology is the most significant characteristic for hydrogeological assessment (Khan et al, 2022 ). The potential recharge and movement of an aquifer are influenced by geology, geomorphology, and structures (Butler et al, 2002; Kumar et al, 2022). The geomorphological map created depicts the landforms and topography of a certain area. It is one of the variables that affect groundwater recharge and migration (Githinji et al, 2022 ). The structural origin, Denudational origin and coastal origin geomorphic features are shown in Fig. 4. The Pediment-Pediment complex has assigned the higher value as it covers most of the basin area with gentle slope, which facilitate groundwater recharge. The Moderately Dissected Hills and Valleys having steeper slope ranging from 16 o - 41 o so infiltration of groundwater is possibly least, so the lower weightages is assigned to it. The study of various morphological components is critical for assessing the artificial groundwater recharge zones because it controls groundwater flow on the surface and subsurface (Kumar and Krishna 2016). Slope Low lying regions are good for runoff water recharge because they allow more time for retention and infiltration (Rahman et al. 2012; Rajaveni et al. 2017). The gradient of the ground had an opposite relationship with runoff penetration into ground (Khan et al, 2022 ; Nag et al, 2022; Githinji et al, 2022 ). Steep slopes eventually have faster runoff but high erosion with low recharge capacity (Magesh et al., 2011a, b). In the Mochemad River Basin major part is gently sloping and flat land which influences the infiltration rate and surface runoff. The overall slope of the basin is generally trending from northeast to southwest, as seen in Fig. 5. Highest rank is given to the area having low gradient (0 o - 4 o ) and the ranks were gradually decreased as the degree of slope increased. The steeper slope ranging from 16o-41o was assigned lowest rank. It’s unsuitable for groundwater recharge due to high gradient and associated runoff (Mahato et al, 2022). Drainage Density: Drainage density and Groundwater recharge are inversely correlated (Halder et al. 2020; Nag et al., 2022). In other words, groundwater recharge is less likely in places with high drainage densities and vice versa (Mandal et al. 2016; Luo et al, 2020; Khan et al, 2022 ). Drainage was extracted using GDEM and then digitized and verified using topographic map of Mochemad River Basin. The Density tool in ArcGIS was used to determine the drainage density. Five drainage density classes are used to classify the Basin area: very low density of drainage (0- 0.5 km/km2), low drainage density (0.5–1.5 km/km2), moderate drainage density (1.5–2.5km/km2), high drainage density (2.5–3.5 km/km2) and very high drainage density (3.5–6.5 km/km2). The high drainage density is given low ranks while low drainage density has high rank Soil Type Soil is the most important component in identifying the artificial groundwater recharge zones. Soil-landscape relationship depicting the association of shallow soil in hills (Entisols) and medium soil in narrow valleys (Inceptisols) in the research region (Bhattacharyya et al, 2020). The soil map was created with the assistance of the National Bureau of Soil Survey and Land Use Planning (NBSS & LUP), India, as well as field acquired data. The Mochemad River Basin is predominantly covered by loamy soil, which is observed on undulating slope, at foothill and on the hill. Loamy soil on undulating soil has been assigned the highest rank as it is facilitating more infiltration. While clayey soil has been assigned lowest rank as it clay is impermeable Lineament density Linear or curvilinear surficial expression of geological aspects like joints, fractures and faults are the lineaments (Luo et al, 2020; Khan et al, 2022 ). Lineaments enhance secondary porosity of the country rock which facilitates the recharge of groundwater (Haridas et al. 1998; Nag and Saha, 2014)). The Lineament map of Mochemad River Basin is extracted from web version of ‘Manual for Geomorphology and Lineament mapping’ by adding and digitizing WMS layer. Then Lineament density layer was derived using density tool in ArcMap. The lineament density map (Fig. 6) was classified into five categories: very low (0- 0.5 km/km2), low (0.5 -1 km/km2), moderate (1-1.5km/km2), high (1.5–2 km/km2) and very high (2–2.6 km/km2). The Lineament density of the basin displays that most area is suitable for artificial recharge (Shailaja et al, 2019). Higher the Lineament density, high is the recharge. So, the area having high lineament density is ranked high and low lineament density was ranked low (Mahato et al, 2022). Land Use and Land Cover (LULC) LULC is another key factor that influences the hydro-geological processes and recharge of groundwater (Selvam et al. 2014; Luo et al, 2020). Various LULC patterns of the Mochemad River Basin were derived from LISS III satellite image. The study area has diverse LULC such as Scrubland (7.1 km2), Cropland (35.33 km2), Plantation (48.68 km2), Deciduous Broadleaf Forest (6.23 km2), Mixed Forest (21.27 km2), Evergreen Broadleaf Forest (8.89 km2) and Waterbody (2.7 km2). Weights to each feature in the land use/land cover map can be assigned relative to the water holding capacity (Deepa et al., 2016). The most dominant LULC categories are Cropland and Plantation which are irrigated and considered most suitable area for the groundwater recharge as it favours infiltration of irrigated as well as rainwater (Rejith et al. 2019; Luo et al, 2020). Therefore, Plantation and Cropland have been assigned highest rank while the scrubland which constitutes very small area have been assigned lowest rank. Weight Calculation Using AHP: Saaty (1987) created the AHP, a Multi-Criteria choice-Making (MCDM) technique that is frequently used to analyse spatial choice problems, such as groundwater difficulties (Rejith et al., 2019; Zolekar and Bhagat, 2015). The weight of several layers is evaluated using the AHP approach. Using Saaty's scale (1–5) of relative significance, a Pairwise Comparison Matrix (PCM) is initially constructed (Saaty, 1987). Table.1. Pairwise Comparison Matrix Criteria Geology Geomorphology Slope Drainage Density Soil Type Lineament Density LULC Geology 1 2 3 4 5 6 7 Geomorphology 0.5 1 2 3 4 5 6 Slope 0.33 0.5 1 2 3 4 5 Drainage Density 0.25 0.33 0.5 1 2 3 4 Soil Type 0.2 0.25 0.33 0.5 1 2 3 Lineament Density 0.16 0.2 0.25 0.33 0.5 1 2 LULC 0.14 0.16 0.2 0.25 0.33 0.5 1 Table.2.Normalized pairwise comparison matrix Criteria Geology Geomorphology Slope Drainage Density Soil Type Lineament Density LULC Geology 0.39 0.45 0.41 0.36 0.32 0.28 0.25 Geomorphology 0.19 0.22 0.27 0.27 0.25 0.23 0.21 Slope 0.13 0.11 0.14 0.18 0.19 0.19 0.18 Drainage Density 0.10 0.07 0.07 0.09 0.13 0.14 0.14 Soil Type 0.08 0.06 0.05 0.05 0.06 0.09 0.11 Lineament Density 0.06 0.04 0.03 0.03 0.03 0.05 0.07 LULC 0.06 0.04 0.03 0.02 0.02 0.02 0.04 In this AHP model of Mochemad River Basin, the value of consistency ratio (CR) is 0.02 (λ = 0.02, n = 7, RI = 1.32, CI = 0.03). This shows a good consistency in the pairwise comparison matrix. Therefore, the technique of AHP applied in the present study shows reasonably precise results for artificial groundwater recharge zones. Potential zones for Groundwater recharge The systematic analysis of RS, GIS and AHP techniques on weighted parameters are applied to delineate the groundwater recharge zones in ArcGIS environment. The Normalised Pairwise comparison matrix was obtained by dividing the individual weight by the summation of the weights of each parameter. Table 3 Weights of the criterion used for AHP method Sr. No. Criteria Sub Criteria Normalised Weight Weighted Influence (%) Assigned Weightage 1 Geology Amphibolite 0.35 35 4 Ultramafite 4 BIF 4 Dolerite Dyke 3 Quartz Vein 4 Biotite Gneiss 5 Fluvio- marine deposits 5 Meta Gabbro 3 Meta- pellite 2 Granite Gneiss 3 Laterite 1 TTG Gneiss 5 2 Geomorphology Pediment- Pediplain Complex 0.24 24 5 Younger Coastal Plain 4 Moderately Dissected Lower Plateau 2 Moderately Dissected Hills and Valleys 1 Moderately Dissected Lower Plateau 3 3 Slope 1. (0 o - 4 o ) 0.16 16 5 2. (4 o -9 o ) 4 3. (9 o -16 o ) 3 4. (16 o – 41 o ) 1 4 Drainage Density 1. (0- 0.5 km/km 2 ) 0.1 10 5 2. (0.5–1.5 km/km 2 ) 4 3. (1.5–2.5km/km 2 ) 3 4. (2.5–3.5 km/km 2 ) 2 5. (3.5–6.5 km/km 2 ) 1 5 Soil Type Clayey Soil 0.07 7 1 Loamy Soil- Undulating Slope 5 Loamy Soil- Foot hill 2 Loamy Soil- Hills 3 6 Lineament Density 1. (0- 0.5 km/km 2 ) 0.05 5 1 2. (0.5–1 km/km 2 ) 2 3. (1–1.5km/km 2 ) 3 4. (1.5–2 km/km 2 ) 4 5. (2–2.6 km/km 2 ) 5 7 LULC Evergreen Broadleaf forest 0.03 3 4 Scrubland 1 Cropland 5 Waterbody 5 Mixed Forest 3 Decidous Broadleaf forest 2 Plantation 5 Geology has the highest normalised weight followed by geomorphology, slope, drainage density, Soil type, Lineament density and LULC. The potential artificial recharge zone map is classified into four classes namely Unsuitable (18.29 km 2 ), moderately suitable (24.73 km 2 ), highly suitable (71.86 km 2 ) and very high suitable (13.57 km 2 ). Table. Areas wise suitability for the recharge zone Categories Area (km 2 ) Percentage Unsuitable 18.29 14.24 Moderately Suitable 24.73 19.25 Highly Suitable 71.86 55.94 Very Highly suitable 13.57 10.56 The artificial groundwater recharge zone map in figure no. 9 shows the zones for artificial recharge of groundwater demarcated using overlayed analysis in the ArcGIS. The zone with low potential artificial recharge lies in the north-east and south-west part of the study region which comprises 14.24% of the total area. This area includes Malgaon, Nhaichiad, Mochemad, Ansur and Tulas village where most of the rock type is Laterite and Granite Gneiss with steeply sloping topography. Laterite and Granite Gneisses with steep slopes and moderately dissected Hills, valleys and plateaus combine hamper the infiltration rate. While vast area has highly suitable artificial groundwater recharge zone and Very Highly suitable potential zone of groundwater recharge exist in central part of the Mochemad River Basin. In this analysis, suitable site for constructing artificial recharge and groundwater conservation structures are identified. Various artificial recharge techniques like surface spreading and construction of check dams, percolation ponds/tanks, bench trenching, contour barriers and surface irrigation can be employed in the demarcated favourable zones. Such structures will lower the surface runoff and enhance the infiltration rate (Bhattacharya, 2010). Validation The validation of results was carried to ensure the precision of the potential groundwater recharge zones through the fieldwork. The artificial groundwater recharge zones of unsuitable, moderately suitable, highly suitable and Very Highly suitable zones were validated with groundwater level data in the Mochemad watershed. The 39 wells were monitored for groundwater level in the month of December 2021. It is clear that highly suitable zones for artificial recharge are located in central and northern part of Mochemad River basin. Out of 39 monitored wells, 33 are falling under highly suitable zone while 3 each are falling in moderately suitable and unsuitable zone. The groundwater levels in the highly suitable zone are ranging from 0.38 m bgl to 2.83 m bgl while the groundwater level in the moderately suitable zone is ranging from 3.35 m bgl to 3.96 m bgl. And the unsuitable zone has the groundwater levels higher than 5m bgl. The location of Adeli dam which is constructed over Mochemad River was compared with the resultant Artificial Groundwater Recharge zone map. The dam lies in the moderately suitable zone for artificial recharge of groundwater. The areas having low infiltration are favourable for surface water harvesting structures like dams and lakes (Mahmoud et al., 2014). Hence, the location of Adeli dam in the moderately suitable zone verifies the accuracy of the map prepared for Artificial Groundwater Recharge using AHP and GIS technique. The precision of the AHP based model was also evaluated by Receiver Operating Characteristic (ROC) curve as shown in Fig. 12 . Artificial groundwater recharge zones map of Mochemad River Basin has been validated by 65 wells. The relation between the model accuracy and area under the curve (AUC) is summarised in five groups: Excellent (0.9-1), very good (0.8–0.9), good (0.7–0.8), average (0.6–0.7) and poor (0.5–0.6). The graph of false positive rate indicating specificity versus true positive rate indicating sensitivity is plotted. Then, the Area under Curve (AUC) was calculated by following formula: AUC= \(\:\sum\:_{i=1}^{n=4}\frac{(X2+X1)}{2(Y2-Y1)}\) , Where AUC denotes area under curve, X denotes the cumulative area of different artificial groundwater recharge zones, Y indicates cumulative number of wells in each recharge zone, 1 and 2 are two consecutive values in data and n is the number of zones. According to ROC curve plot, the area under the curve (AUC) value is 0.86, which is referred to 86% of accuracy. This indicates that the implied method for demarcating the artificial groundwater recharge zones is reliable and has very good accuracy. Conclusion The Mochemad River Basin in Sindhudurg district, Maharashtra, experiences heavy rainfall but faces the challenge of saline water intrusion due to its coastal location. Key factors influencing groundwater recharge in the basin include Geology, Geomorphology, Slope, Drainage Density, Soil Type, Lineament Density, and LULC. Using RS, GIS, and AHP techniques, thematic layers were created from datasets such as SRTM DEM, LISS III, GSI, and NRSC (2012). The assigned weights were normalized using the AHP method and overlaid in ArcGIS to generate an artificial groundwater recharge map. The basin was classified into four recharge suitability zones: unsuitable (14.24%, 18.29 km²), moderately suitable (19.25%, 24.73 km²), highly suitable (55.94%, 71.86 km²), and very highly suitable (10.56%, 13.87 km²). Highly suitable zones include Gavdevadi, Vajrat, Talavda, and Matond, while hilly terrains in Malgaon, Ansur, Mochemad, Nhaichiad, Jasoli, and Asolipal are prioritized for water conservation structures like check dams, groundwater dams, CCT, MNB, and CNB. The ROC curve analysis validated the method, yielding an AUC accuracy of 84.6%, confirming the reliability of the GIS-based AHP approach. The study highlights the efficiency of RS, GIS, and AHP techniques in reducing time and effort compared to traditional methods. These findings serve as a valuable guideline for groundwater recharge planning and sustainable water resource management in the region. Declarations Author Contribution: Tejas S. Naik: Collects data, performs GIS-based analysis, and prepares thematic maps. Satyajit K. Gaikwad: Conducts Analytical Hierarchy Process (AHP) modeling and statistical analysis. Vasant M. Wagh: Interprets hydrogeological parameters and validates recharge potential zones. Praveen N. Kamble: Assists in data processing, cartographic visualization, and manuscript drafting. Ajaykumar K. Kadam (Corresponding Author): Supervises the research, reviews the manuscript, and provides critical revisions and improvements Funding : This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Clinical trial number: Not applicable. Data availability: The datasets analysed during the current study are available from the corresponding author on reasonable request. Consent to Publish declaration : Not applicable Consent to Participate declaration: Not applicable Acknowledgements: The authors wish to thank all who assisted in conducting this work. Conflict of interest: The authors declare no financial or non-financial interests exist that are directly or indirectly related to this work. Ethics declaration: The authors confirm that all the research meets ethical guidelines. References Githinji, T. W., Dindi, E. 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In Sustainable resource management in mining with special reference to coastal regions of Karnataka and Maharashtra. Mining Engineers Association of India, Belgaum Chapter Workshop (pp. 8-10). Kadam, A. K., Patil, S. N., Gaikwad, S. K., Wagh, V. M., Patil, B. D., & Patil, N. S. (2023). Demarcation of subsurface water storage potential zone and identification of artificial recharge site in Vel River watershed of western India: integrated geospatial and hydrogeological modeling approach. Modeling Earth Systems and Environment , 9 (3), 3263-3278. Sahu, U., Wagh, V., Mukate, S., Kadam, A., & Patil, S. (2022). Applications of geospatial analysis and analytical hierarchy process to identify the groundwater recharge potential zones and suitable recharge structures in the Ajani-Jhiri watershed of north Maharashtra, India. Groundwater for Sustainable Development , 17 , 100733. Yazdi, S. H., Robati, M., Samani, S., & Hargalani, F. Z. (2024). Assessment of groundwater sustainability in arid and semi-arid regions using a fuzzy Delphi method. International Journal of Environmental Science and Technology , 1-22. Howlader, R., Chowdhury, M. M. A., Jahan, C. S., Hossain, M. A., Rahaman, M. F., Ghose, B. K., & Islam, M. (2024). Delineation of fresh groundwater potentiality zones in saline coastal aquifers, Southwest Bangladesh using remote sensing and GIS approaches. Environmental Geochemistry and Health , 46 (11), 454. Pitchaimani, V. S., Joe, R. J., Shyamala, G., Manjula, G., Hemalatha, B., Babu, M. D., ... & Ravindran, G. (2024). Multivariate statistical and hydrogeochemical analysis of seasonal groundwater quality variations in coastal villages of Trivandrum district, south India. Discover Sustainability, 5( 1), 1-32. Additional Declarations No competing interests reported. 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Gaikwad","email":"","orcid":"","institution":"Savitribai Phule Pune University","correspondingAuthor":false,"prefix":"","firstName":"Satyajit","middleName":"K.","lastName":"Gaikwad","suffix":""},{"id":454869029,"identity":"f232d160-02de-41c6-92d2-1cc5b8276ff4","order_by":2,"name":"Ajaykumar K. Kadam","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABFElEQVRIiWNgGAWjYBACCQYGAyDFxsDAA+HLgcgDD0jQYmEM1pJAWAsDTEtFYgOIwqdFsv3wxk83avjkDc6cMd3wc49E+vywww+BttjJ6TZg1yLNk1YsnXOMzXDD2R6zmz3PJHI33k4zAGpJNjY7gF2LHEOOgXQOGxvjhvM8Zjd4DgC1zE4AaTmQuA2XFv43xr9z/rHZg7Tc/HNAIt1wdvoHvFqkJXLMpHPb2BJBDrsNtCVBXjoHvy2SM56VWef2sSXPPHOs7LbMAQnDDdI5BQcSDHD7ReJ88ubbOd+O2fadSd52882BOnn52embP3yosJPDpQUKjjEowBQYgBkGeJWDQA2DfAOUCWeMglEwCkbBKIACAProZvNl7VGVAAAAAElFTkSuQmCC","orcid":"","institution":"Kavayitri Bahinabai Chaudhari North Maharashtra University","correspondingAuthor":true,"prefix":"","firstName":"Ajaykumar","middleName":"K.","lastName":"Kadam","suffix":""},{"id":454869030,"identity":"5c9ff2fc-fdaf-43f5-853d-d51c593847ef","order_by":3,"name":"Vasant M Wagh","email":"","orcid":"","institution":"SRTM University","correspondingAuthor":false,"prefix":"","firstName":"Vasant","middleName":"M","lastName":"Wagh","suffix":""},{"id":454869031,"identity":"e7ec111a-9fc3-4da6-89db-9d30c6a7fc1f","order_by":4,"name":"Praveen. 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2012)\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6331621/v1/3f557f5863b8b7ebd410cdd4.png"},{"id":82610680,"identity":"1aa3a57f-e760-46d1-9099-a7f5cdc2c361","added_by":"auto","created_at":"2025-05-13 10:41:16","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":79716,"visible":true,"origin":"","legend":"\u003cp\u003eGeomorphology Map (modified after GSI and NRSC, 2012.)\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6331621/v1/4ac7047cbb2ec2689d3b69e2.png"},{"id":82610688,"identity":"98656ecc-522f-4f30-9df3-71800316de05","added_by":"auto","created_at":"2025-05-13 10:41:16","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":142039,"visible":true,"origin":"","legend":"\u003cp\u003eSlope Map\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-6331621/v1/b0584edc1d6005728a56e520.png"},{"id":82611974,"identity":"228aef20-46ca-42d4-a09a-d20ea34baf59","added_by":"auto","created_at":"2025-05-13 10:57:16","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":113771,"visible":true,"origin":"","legend":"\u003cp\u003eDrainage Density\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-6331621/v1/8e1edd5d976d3cfdbd97f19a.png"},{"id":82611057,"identity":"80bba529-e483-421e-993a-c421899df678","added_by":"auto","created_at":"2025-05-13 10:49:16","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":71936,"visible":true,"origin":"","legend":"\u003cp\u003eSoil Map\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-6331621/v1/0f95b3682e1b7d7611542d4f.png"},{"id":82610715,"identity":"f99c2f29-8a09-4a1e-93dd-9e3283101085","added_by":"auto","created_at":"2025-05-13 10:41:17","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":130826,"visible":true,"origin":"","legend":"\u003cp\u003eLineament Density Map\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-6331621/v1/34776639cdadafc93b8f78e2.png"},{"id":82610693,"identity":"dec57d8b-3ea1-4ff1-96fa-dc3164486f7b","added_by":"auto","created_at":"2025-05-13 10:41:16","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":102023,"visible":true,"origin":"","legend":"\u003cp\u003eLULC Map.\u003c/p\u003e","description":"","filename":"floatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-6331621/v1/4b3f9968bf4785ab3c875105.png"},{"id":82611064,"identity":"6483a752-07e1-4da4-89bb-a24909ad6f3a","added_by":"auto","created_at":"2025-05-13 10:49:16","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":100686,"visible":true,"origin":"","legend":"\u003cp\u003eArtificial Groundwater Recharge Zone Map\u003c/p\u003e","description":"","filename":"floatimage10.png","url":"https://assets-eu.researchsquare.com/files/rs-6331621/v1/e10dae145a57a93ec4457bad.png"},{"id":82611059,"identity":"189966a9-d8ec-4276-a91f-26883fff5097","added_by":"auto","created_at":"2025-05-13 10:49:16","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":310493,"visible":true,"origin":"","legend":"\u003cp\u003eArtificial surface storage and groundwater recharge structures map\u003c/p\u003e","description":"","filename":"floatimage11.png","url":"https://assets-eu.researchsquare.com/files/rs-6331621/v1/112dac6eafa739a6bb31ab44.png"},{"id":82610700,"identity":"d80c3804-3d66-4087-a581-6e0ac11d85d6","added_by":"auto","created_at":"2025-05-13 10:41:16","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":57318,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver Operating Characteristic (ROC) curve for groundwater potential map validation.\u003c/p\u003e","description":"","filename":"floatimage12.png","url":"https://assets-eu.researchsquare.com/files/rs-6331621/v1/b061512fb5091bc7a2289726.png"},{"id":84940855,"identity":"c3f9dffe-3f16-4d18-a1f7-0981f3ef1c31","added_by":"auto","created_at":"2025-06-19 05:08:53","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2469578,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6331621/v1/3a4feb3d-0d01-455e-9f49-f0a9958d7245.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Modelling and Mapping of Artificial Groundwater Recharge Zones using Geospatial Techniques and Analytical Hierarchical Process (AHP) of Mochemad River Basin, Sindhudurg district, Maharashtra, India","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGroundwater is the utmost essential and trustworthy water reserve used for drinking and irrigation as well as for improvement of the socio-economic structure of any region (Kumari and Singh, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, Howlader et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Yazdi et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Groundwater constitutes about 95% of total freshwater available on the earth (Freeze and Cherry, 1979). The worldwide problem of availability of fresh groundwater resource is of major concern. The disparity between finite groundwater resources and increasing its exploitation making the condition critical/worst (Kumari and Singh, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Sahu et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In coastal areas, due to over- exploitation of groundwater leading to drinking water shortage as well as intrusion of seawater (Bhattacharya, 2010, Rajasekhar et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Rise and fall of water level on decadal scale showing stressful groundwater scenario in the southern Konkan, of which study area is a part (Das, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e,). Increasing sea level will be affecting coastal aquifers badly (Gaikwad et al, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Hence it is very much essential to understand various ways to recharge groundwater to improve its level for sustainable livelihood (Pitchaimani et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eArtificial recharge of groundwater by way of recharging structures is one of the efficient methods for groundwater management (Kaliraj, 2013). Therefore, it is important to augment the subsurface water possible by man-made recharge using geospatial techniques (Kumari and Singh, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Many qualitative and quantitative techniques like Multi-Influencing Factor, Analytical Hierarchy Process (AHP) AHP, Boolean logic, Fuzzy logic and WetSpa was utilized for identifying artificial groundwater recharge zones (Saaty, 1980; Zaidi et. Al. 2015; Patel et al, 2022). The present is an initial attempt, for artificial groundwater recharge zones (AGRZ) using geospatial modelling and mapping using Multicriteria based AHP technique. The AHP prepares a pursue problem into a order and used for solving many complex problems related to decision making (Saaty 1988; Pishyar et al. 2020; Kumari and Singh, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Patel et al, 2022). Recently many studies for the identification of groundwater potential zones have been carried out using GIS and remote sensing (Kolekar et al., 2017; Murmur et al., 2019; Dar et al., 2020; Kumari and Singh, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). A map classifying low, moderate and high groundwater potential zones is generated using various parameters and their assigned weightages (Kadam et al, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Rainwater collection sites were found for groundwater recharge, allowing for better development, planning, and management of water resources. In the area under investigation no study has been carried out and/or reported for the identification of artificial water conservation sites using AHP techniques. This work is the first to undertake a longitudinal analysis in the Hodawada river basin. This paper investigates the usefulness of the GIS and AHP techniques in demarcation of artificial recharge structure in geologically varied lithologies. This research seeks to address the solution to the problem of groundwater availability and scarcity observed after February in varied geological formations in the Mochemad river basin. A holistic approach is utilized integrating various parameters with their weightages for AHP analysis and for locating groundwater recharge structures. The techniques used here can be utilized in the area having similar geology and geomorphology especially in the coastal parts.\u003c/p\u003e\n\u003ch3\u003eStudy Area\u003c/h3\u003e\n\u003cp\u003eThe area under investigation is the Mochemad River Basin present in the Sindhudurg district, southerly part of the Konkan area, west coast of India. The basin is spread in Kudal, Sawantwadi and Vengurla Tehsils of Sindhudurg district. It is bounded by Longitudes 73\u003csup\u003e0\u003c/sup\u003e 39\u0026rsquo; to 73\u003csup\u003eo\u003c/sup\u003e 49\u0026rsquo; E to Latitudes 15\u003csup\u003eo\u003c/sup\u003e 47\u0026rsquo; N to 15o 57\u0026rsquo; N and is included in the Survey of India\u0026rsquo;s Toposheet no. 48 E/9 and 48 E/13 of scale 1:50,000. The river Mochemad originates in the hills of Humras village (elevation of 131 mts) near Kudal and flows SW through Vengurla tehsil and meets Arabian Sea near Tak village (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The Mochemad River has the total length of 28 km and basin area of about 130 km2. The rainfall ranges between 3000 and 4700 mm/year (Bandaru et al, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The basin receives around 3070 mm of rain on average (CGWB, 2014). According to physiography, the eastern portion of the river is made up of flat-topped hills with a Laterite-covered undulating plateau, while the western portion is the coastal plain (CGWB, 2014). Laterites, granites, and granitic gneisses are the main aquifer formations. The groundwater yields between 2 and 5 m\u003csup\u003e3\u003c/sup\u003e/day and is found under an unconfined aquifer at moderately shallow depths ranging from 2 to 10 m bgl. Both bore and dug wells typically have a depth of 2 to 11 meters and a yield of 2 to 5 meters per day in coastal alluvium. While the bore wells are 50 to 70 meters deeper and have yields ranging from 500 to 7770 litres per hour, the wells in the Gneissic complex are 3 to 11 meters deep and have a yield of 2 to 3 m3/day. The laterite found in the basin's northeast and south can have specific capacities ranging from 79.10 to 424.57 litres per minute of drawdown, transmissivities between 46.59 and 375.22 m\u003csup\u003e2\u003c/sup\u003e/day, and permeabilities between 7.40 to 425.22 m/day (CGWB, 2014).\u003c/p\u003e "},{"header":"Method and Material","content":"\u003cp\u003eThe multi-parametric analysis for demarcating artificial groundwater recharge areas of Mochemad watershed has been done using AHP technique in GIS environment. The current study is implemented in the following methods (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In the study of the Mochemad River Basin, seven criterions such as Geology (GG), Geomorphology (GM), Drainage Density (DD), Slope (S), Lineament density (LD), Soil type and LULC have been analysed by AHP approach using normalized weight to demarcate area for artificial groundwater recharge. Seven spatial criterions like Geology (GG), Geomorphology (GM), Drainage Density (DD), Slope (S), Lineament density (LD), Soil type and LULC have been used for the preparation of geospatial database. Lithological and geomorphological map was prepared using district resource map of Sindhudurg district. The Survey of India (SOI) toposheet, from which the drainage density layer was created, was used to digitise the stream network. To create a slope map, SRTM DEM was taken from US Geological Survey Earth Explorer and processed. The \"Manual for Geomorphology and Lineament mapping (web version)\" was used to digitise the lineament map, which was then processed to determine its density. LISS 3 satellite imagery was used to create the Land Use and Land Cover (LULC) map, and the National Bureau of Soil Survey and Land Use Planning (NBSS and LUP, Nagpur) map was used to create the soil type map layer (Reshmidevi et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTable.1: Details of the data used for the study their source\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"3\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eData Type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSources\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eData used for\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTopographical maps No.\u003c/p\u003e \u003cp\u003e48 E/9 and 48 E/13\u003c/p\u003e \u003cp\u003e(Scale 1:50,000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSurvey of India (SOI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDrainage map\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGEDM\u003c/p\u003e \u003cp\u003e(Resolution\u0026thinsp;=\u0026thinsp;30m)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGlovis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRelief, slope, drainage, topographic\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGeological map\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGSI District Resource Map,\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGeology\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGeomorphological map\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBHUVAN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGeomorphology\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLULC Data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLISS- III satellite imagery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLULC Map\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSoil data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNational Bureau of Soil Survey and land Use Planning\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSoil type\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLineament Data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eToposheet map and GSI map\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLineament density and map.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroundwater fluctuation data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eField data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGroundwater level\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 \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMulti- criteria Decision making using Analytical Hierarchical process (AHP)\u003c/h3\u003e\n\u003cp\u003eIn this study of Mochemad River Basin, AHP is utilized to demarcate the regions for artificial groundwater recharge. The method was suggested by Saaty (1980) for solving complicated decision-making issues. Field study and experts view was used to assign the weight of the Saaty\u0026rsquo;s 1\u0026ndash;9 scale for each thematic layer. The response of these influencing parameters is weighted as per their reaction to recharge of groundwater. A factor with high rank is the layer with high impact and factor with low rank are with low impact on the groundwater recharge. AHP was used for assigning the weights and calculate the normalized weights for the parameters influencing groundwater recharge. The Pairwise Comparison Matrix of thematic layers such as Geology, Geomorphology, Slope, Drainage Density, Soil Type, Lineament Density and LULC were compared according to the 1\u0026ndash;9 scale suggested by Saaty (2008). The consistency ratio (CR) and consistency index (CI) values were computed to examine the reliability of the obtained results.\u003c/p\u003e\u003cp\u003eThe formula used is\u003c/p\u003e\n\u003cdiv class=\"Heading\"\u003eCR \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:=\\frac{\\text{C}\\text{I}}{\\text{R}\\text{I}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\n\u003cp\u003eWhere CR is consistency ratio, RI is random consistency index whose values are derived from the order of matrix. CI is consistency index which is calculated from the formula given below\u003c/p\u003e\n\u003cdiv class=\"Heading\"\u003eCI \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:=\\frac{\\:{\\lambda\\:}-\\text{n}}{\\text{n}-1}\\)\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\n\u003cp\u003eWhere \u0026lambda; is the principal Eigen value of the matrix and n is the number of parameters affecting groundwater recharge. The CR should be less than 0.1 to avoid the inconsistency.\u003c/p\u003e"},{"header":"Result and Discussion","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eGeology:\u003c/h2\u003e\n \u003cp\u003eThe porosity and permeability of rocks determine the presence and flow of groundwater (Balaji et al. 2019; Luo et al, 2020; Khan et al, \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). There is a variety of lithology in the research region (Deendar, \u003cspan class=\"CitationRef\"\u003e2003\u003c/span\u003e). Eleven distinct geological units, ranging in age from Pre-Cambrian to modern, make up the river basin under study (Gaikwad et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). A suite of Tonalite, Trondjhemite, and Granodiorite (TTG) gneisses are mostly exposed, together with granitoids and migmatites that contain enclaves of Banded Iron Formation (BIF), Amphibolite, and Ultramafite. Dolerite dykes and quartz veins penetrate the biotite TTG, which is the basement for the supracrustal rocks in the region, such as Metapellite and BIF. Laterite is developed over the country rock at higher elevations and fluvio- marine sediments are present near the mouth of the river (GSI, 2012). The homogeneously foliated Biotite-TTG gneiss and fluvio-marine deposits demonstrates high infiltration capacity and hence given highest rank (Abijith et al. 2020; Mahato et al, 2022). Both primary and secondary laterite are found in large quantities in low-lying platens and along the Western coast. They are porous, although their impermeability will limit infiltration. As a result, it has the lowest ranking.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eGeomorphology\u003c/h3\u003e\n\u003cp\u003eGeomorphology is the most significant characteristic for hydrogeological assessment (Khan et al, \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). The potential recharge and movement of an aquifer are influenced by geology, geomorphology, and structures (Butler et al, 2002; Kumar et al, 2022). The geomorphological map created depicts the landforms and topography of a certain area. It is one of the variables that affect groundwater recharge and migration (Githinji et al, \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). The structural origin, Denudational origin and coastal origin geomorphic features are shown in Fig.\u0026nbsp;4. The Pediment-Pediment complex has assigned the higher value as it covers most of the basin area with gentle slope, which facilitate groundwater recharge. The Moderately Dissected Hills and Valleys having steeper slope ranging from 16\u003csup\u003eo\u003c/sup\u003e- 41\u003csup\u003eo\u003c/sup\u003e so infiltration of groundwater is possibly least, so the lower weightages is assigned to it. The study of various morphological components is critical for assessing the artificial groundwater recharge zones because it controls groundwater flow on the surface and subsurface (Kumar and Krishna 2016).\u003c/p\u003e\n\u003ch3\u003eSlope\u003c/h3\u003e\n\u003cp\u003eLow lying regions are good for runoff water recharge because they allow more time for retention and infiltration (Rahman et al. 2012; Rajaveni et al. 2017). The gradient of the ground had an opposite relationship with runoff penetration into ground (Khan et al, \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e; Nag et al, 2022; Githinji et al, \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). Steep slopes eventually have faster runoff but high erosion with low recharge capacity (Magesh et al., 2011a, b). In the Mochemad River Basin major part is gently sloping and flat land which influences the infiltration rate and surface runoff. The overall slope of the basin is generally trending from northeast to southwest, as seen in Fig.\u0026nbsp;5. Highest rank is given to the area having low gradient (0\u003csup\u003eo\u003c/sup\u003e- 4\u003csup\u003eo\u003c/sup\u003e) and the ranks were gradually decreased as the degree of slope increased. The steeper slope ranging from 16o-41o was assigned lowest rank. It\u0026rsquo;s unsuitable for groundwater recharge due to high gradient and associated runoff (Mahato et al, 2022).\u003c/p\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eDrainage Density:\u003c/h2\u003e\n \u003cp\u003eDrainage density and Groundwater recharge are inversely correlated (Halder et al. 2020; Nag et al., 2022). In other words, groundwater recharge is less likely in places with high drainage densities and vice versa (Mandal et al. 2016; Luo et al, 2020; Khan et al, \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eDrainage was extracted using GDEM and then digitized and verified using topographic map of Mochemad River Basin. The Density tool in ArcGIS was used to determine the drainage density. Five drainage density classes are used to classify the Basin area: very low density of drainage (0- 0.5 km/km2), low drainage density (0.5\u0026ndash;1.5 km/km2), moderate drainage density (1.5\u0026ndash;2.5km/km2), high drainage density (2.5\u0026ndash;3.5 km/km2) and very high drainage density (3.5\u0026ndash;6.5 km/km2). The high drainage density is given low ranks while low drainage density has high rank\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003eSoil Type\u003c/h2\u003e\n \u003cp\u003eSoil is the most important component in identifying the artificial groundwater recharge zones. Soil-landscape relationship depicting the association of shallow soil in hills (Entisols) and medium soil in narrow valleys (Inceptisols) in the research region (Bhattacharyya et al, 2020). The soil map was created with the assistance of the National Bureau of Soil Survey and Land Use Planning (NBSS \u0026amp; LUP), India, as well as field acquired data. The Mochemad River Basin is predominantly covered by loamy soil, which is observed on undulating slope, at foothill and on the hill. Loamy soil on undulating soil has been assigned the highest rank as it is facilitating more infiltration. While clayey soil has been assigned lowest rank as it clay is impermeable\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003eLineament density\u003c/h2\u003e\n \u003cp\u003eLinear or curvilinear surficial expression of geological aspects like joints, fractures and faults are the lineaments (Luo et al, 2020; Khan et al, \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). Lineaments enhance secondary porosity of the country rock which facilitates the recharge of groundwater (Haridas et al. 1998; Nag and Saha, 2014)). The Lineament map of Mochemad River Basin is extracted from web version of \u0026lsquo;Manual for Geomorphology and Lineament mapping\u0026rsquo; by adding and digitizing WMS layer. Then Lineament density layer was derived using density tool in ArcMap. The lineament density map (Fig.\u0026nbsp;6) was classified into five categories: very low (0- 0.5 km/km2), low (0.5 -1 km/km2), moderate (1-1.5km/km2), high (1.5\u0026ndash;2 km/km2) and very high (2\u0026ndash;2.6 km/km2). The Lineament density of the basin displays that most area is suitable for artificial recharge (Shailaja et al, 2019). Higher the Lineament density, high is the recharge. So, the area having high lineament density is ranked high and low lineament density was ranked low (Mahato et al, 2022).\u003c/p\u003e\n \u003cdiv\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003eLand Use and Land Cover (LULC)\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eLULC is another key factor that influences the hydro-geological processes and recharge of groundwater (Selvam et al. 2014; Luo et al, 2020). Various LULC patterns of the Mochemad River Basin were derived from LISS III satellite image. The study area has diverse LULC such as Scrubland (7.1 km2), Cropland (35.33 km2), Plantation (48.68 km2), Deciduous Broadleaf Forest (6.23 km2), Mixed Forest (21.27 km2), Evergreen Broadleaf Forest (8.89 km2) and Waterbody (2.7 km2).\u003c/p\u003e\n \u003cp\u003eWeights to each feature in the land use/land cover map can be assigned relative to the water holding capacity (Deepa et al., 2016). The most dominant LULC categories are Cropland and Plantation which are irrigated and considered most suitable area for the groundwater recharge as it favours infiltration of irrigated as well as rainwater (Rejith et al. 2019; Luo et al, 2020). Therefore, Plantation and Cropland have been assigned highest rank while the scrubland which constitutes very small area have been assigned lowest rank.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003eWeight Calculation Using AHP:\u003c/h2\u003e\n \u003cp\u003eSaaty (1987) created the AHP, a Multi-Criteria choice-Making (MCDM) technique that is frequently used to analyse spatial choice problems, such as groundwater difficulties (Rejith et al., 2019; Zolekar and Bhagat, 2015). The weight of several layers is evaluated using the AHP approach. Using Saaty\u0026apos;s scale (1\u0026ndash;5) of relative significance, a Pairwise Comparison Matrix (PCM) is initially constructed (Saaty, 1987).\u003c/p\u003e\n \u003cp\u003eTable.1. Pairwise Comparison Matrix\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tabe\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCriteria\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGeology\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGeomorphology\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSlope\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDrainage Density\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSoil Type\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLineament Density\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLULC\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGeology\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGeomorphology\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSlope\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDrainage Density\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSoil Type\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLineament Density\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLULC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eTable.2.Normalized pairwise comparison matrix\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tabf\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCriteria\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGeology\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGeomorphology\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSlope\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDrainage Density\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSoil Type\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLineament Density\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLULC\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGeology\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGeomorphology\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSlope\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDrainage Density\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSoil Type\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLineament Density\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLULC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eIn this AHP model of Mochemad River Basin, the value of consistency ratio (CR) is 0.02 (\u0026lambda;\u0026thinsp;=\u0026thinsp;0.02, n\u0026thinsp;=\u0026thinsp;7, RI\u0026thinsp;=\u0026thinsp;1.32, CI\u0026thinsp;=\u0026thinsp;0.03). This shows a good consistency in the pairwise comparison matrix. Therefore, the technique of AHP applied in the present study shows reasonably precise results for artificial groundwater recharge zones.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003ePotential zones for Groundwater recharge\u003c/h2\u003e\n \u003cp\u003eThe systematic analysis of RS, GIS and AHP techniques on weighted parameters are applied to delineate the groundwater recharge zones in ArcGIS environment. The Normalised Pairwise comparison matrix was obtained by dividing the individual weight by the summation of the weights of each parameter.\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eWeights of the criterion used for AHP method\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSr. No.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCriteria\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSub Criteria\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNormalised Weight\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eWeighted Influence (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAssigned Weightage\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"12\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"12\"\u003e\n \u003cp\u003eGeology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAmphibolite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"12\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"12\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUltramafite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBIF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDolerite Dyke\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQuartz Vein\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBiotite Gneiss\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFluvio- marine deposits\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMeta Gabbro\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMeta- pellite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGranite Gneiss\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLaterite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTTG Gneiss\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"5\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"5\"\u003e\n \u003cp\u003eGeomorphology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePediment- Pediplain Complex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"5\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"5\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYounger Coastal Plain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModerately Dissected Lower Plateau\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModerately Dissected Hills and Valleys\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModerately Dissected Lower Plateau\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003eSlope\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1. (0\u003csup\u003eo\u003c/sup\u003e- 4\u003csup\u003eo\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"4\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"4\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2. (4\u003csup\u003eo\u003c/sup\u003e-9\u003csup\u003eo\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3. (9\u003csup\u003eo\u003c/sup\u003e-16\u003csup\u003eo\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4. (16\u003csup\u003eo\u003c/sup\u003e \u0026ndash; 41\u003csup\u003eo\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"5\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"5\"\u003e\n \u003cp\u003eDrainage Density\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1. (0- 0.5 km/km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"5\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"5\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2. (0.5\u0026ndash;1.5 km/km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3. (1.5\u0026ndash;2.5km/km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4. (2.5\u0026ndash;3.5 km/km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5. (3.5\u0026ndash;6.5 km/km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003eSoil Type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eClayey Soil\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"4\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"4\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLoamy Soil- Undulating Slope\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLoamy Soil- Foot hill\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLoamy Soil- Hills\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"5\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"5\"\u003e\n \u003cp\u003eLineament Density\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1. (0- 0.5 km/km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"5\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"5\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2. (0.5\u0026ndash;1 km/km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3. (1\u0026ndash;1.5km/km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4. (1.5\u0026ndash;2 km/km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5. (2\u0026ndash;2.6 km/km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"7\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"7\"\u003e\n \u003cp\u003eLULC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEvergreen Broadleaf forest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"7\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"7\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eScrubland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCropland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWaterbody\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMixed Forest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDecidous Broadleaf forest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePlantation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eGeology has the highest normalised weight followed by geomorphology, slope, drainage density, Soil type, Lineament density and LULC. The potential artificial recharge zone map is classified into four classes namely Unsuitable (18.29 km\u003csup\u003e2\u003c/sup\u003e), moderately suitable (24.73 km\u003csup\u003e2\u003c/sup\u003e), highly suitable (71.86 km\u003csup\u003e2\u003c/sup\u003e) and very high suitable (13.57 km\u003csup\u003e2\u003c/sup\u003e).\u003c/p\u003e\n \u003cp\u003eTable. Areas wise suitability for the recharge zone\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tabg\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCategories\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eArea (km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePercentage\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnsuitable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModerately Suitable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHighly Suitable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e71.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e55.94\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVery Highly suitable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.56\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eThe artificial groundwater recharge zone map in figure no. 9 shows the zones for artificial recharge of groundwater demarcated using overlayed analysis in the ArcGIS. The zone with low potential artificial recharge lies in the north-east and south-west part of the study region which comprises 14.24% of the total area. This area includes Malgaon, Nhaichiad, Mochemad, Ansur and Tulas village where most of the rock type is Laterite and Granite Gneiss with steeply sloping topography. Laterite and Granite Gneisses with steep slopes and moderately dissected Hills, valleys and plateaus combine hamper the infiltration rate. While vast area has highly suitable artificial groundwater recharge zone and Very Highly suitable potential zone of groundwater recharge exist in central part of the Mochemad River Basin.\u003c/p\u003e\n \u003cp\u003eIn this analysis, suitable site for constructing artificial recharge and groundwater conservation structures are identified. Various artificial recharge techniques like surface spreading and construction of check dams, percolation ponds/tanks, bench trenching, contour barriers and surface irrigation can be employed in the demarcated favourable zones. Such structures will lower the surface runoff and enhance the infiltration rate (Bhattacharya, 2010).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003ch2\u003eValidation\u003c/h2\u003e\n \u003cp\u003eThe validation of results was carried to ensure the precision of the potential groundwater recharge zones through the fieldwork. The artificial groundwater recharge zones of unsuitable, moderately suitable, highly suitable and Very Highly suitable zones were validated with groundwater level data in the Mochemad watershed. The 39 wells were monitored for groundwater level in the month of December 2021. It is clear that highly suitable zones for artificial recharge are located in central and northern part of Mochemad River basin. Out of 39 monitored wells, 33 are falling under highly suitable zone while 3 each are falling in moderately suitable and unsuitable zone. The groundwater levels in the highly suitable zone are ranging from 0.38 m bgl to 2.83 m bgl while the groundwater level in the moderately suitable zone is ranging from 3.35 m bgl to 3.96 m bgl. And the unsuitable zone has the groundwater levels higher than 5m bgl. The location of Adeli dam which is constructed over Mochemad River was compared with the resultant Artificial Groundwater Recharge zone map. The dam lies in the moderately suitable zone for artificial recharge of groundwater. The areas having low infiltration are favourable for surface water harvesting structures like dams and lakes (Mahmoud et al., 2014). Hence, the location of Adeli dam in the moderately suitable zone verifies the accuracy of the map prepared for Artificial Groundwater Recharge using AHP and GIS technique.\u003c/p\u003e\n \u003cp\u003eThe precision of the AHP based model was also evaluated by Receiver Operating Characteristic (ROC) curve as shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e12\u003c/span\u003e. Artificial groundwater recharge zones map of Mochemad River Basin has been validated by 65 wells. The relation between the model accuracy and area under the curve (AUC) is summarised in five groups: Excellent (0.9-1), very good (0.8\u0026ndash;0.9), good (0.7\u0026ndash;0.8), average (0.6\u0026ndash;0.7) and poor (0.5\u0026ndash;0.6). The graph of false positive rate indicating specificity versus true positive rate indicating sensitivity is plotted. Then, the Area under Curve (AUC) was calculated by following formula:\u003c/p\u003e\n \u003cp\u003eAUC=\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\sum\\:_{i=1}^{n=4}\\frac{(X2+X1)}{2(Y2-Y1)}\\)\u003c/span\u003e\u003c/span\u003e,\u003c/p\u003e\n \u003cp\u003eWhere AUC denotes area under curve, X denotes the cumulative area of different artificial groundwater recharge zones, Y indicates cumulative number of wells in each recharge zone, 1 and 2 are two consecutive values in data and n is the number of zones.\u003c/p\u003e\n \u003cp\u003eAccording to ROC curve plot, the area under the curve (AUC) value is 0.86, which is referred to 86% of accuracy. This indicates that the implied method for demarcating the artificial groundwater recharge zones is reliable and has very good accuracy.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe Mochemad River Basin in Sindhudurg district, Maharashtra, experiences heavy rainfall but faces the challenge of saline water intrusion due to its coastal location. Key factors influencing groundwater recharge in the basin include Geology, Geomorphology, Slope, Drainage Density, Soil Type, Lineament Density, and LULC. Using RS, GIS, and AHP techniques, thematic layers were created from datasets such as SRTM DEM, LISS III, GSI, and NRSC (2012). The assigned weights were normalized using the AHP method and overlaid in ArcGIS to generate an artificial groundwater recharge map. The basin was classified into four recharge suitability zones: unsuitable (14.24%, 18.29 km\u0026sup2;), moderately suitable (19.25%, 24.73 km\u0026sup2;), highly suitable (55.94%, 71.86 km\u0026sup2;), and very highly suitable (10.56%, 13.87 km\u0026sup2;). Highly suitable zones include Gavdevadi, Vajrat, Talavda, and Matond, while hilly terrains in Malgaon, Ansur, Mochemad, Nhaichiad, Jasoli, and Asolipal are prioritized for water conservation structures like check dams, groundwater dams, CCT, MNB, and CNB.\u003c/p\u003e \u003cp\u003eThe ROC curve analysis validated the method, yielding an AUC accuracy of 84.6%, confirming the reliability of the GIS-based AHP approach. The study highlights the efficiency of RS, GIS, and AHP techniques in reducing time and effort compared to traditional methods. These findings serve as a valuable guideline for groundwater recharge planning and sustainable water resource management in the region.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contribution:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTejas S. Naik: Collects data, performs GIS-based analysis, and prepares thematic maps.\u003c/p\u003e\n\u003cp\u003eSatyajit K. Gaikwad: Conducts Analytical Hierarchy Process (AHP) modeling and statistical analysis.\u003c/p\u003e\n\u003cp\u003eVasant M. Wagh: Interprets hydrogeological parameters and validates recharge potential zones.\u003c/p\u003e\n\u003cp\u003ePraveen N. Kamble: Assists in data processing, cartographic visualization, and manuscript drafting.\u003c/p\u003e\n\u003cp\u003eAjaykumar K. Kadam (Corresponding Author): Supervises the research, reviews the manuscript, and provides critical revisions and improvements\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e: This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number:\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability:\u0026nbsp;\u003c/strong\u003eThe datasets analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Publish declaration\u003c/strong\u003e: Not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate declaration:\u003c/strong\u003e Not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u0026nbsp;\u003c/strong\u003eThe authors wish to thank all who assisted in conducting this work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest:\u0026nbsp;\u003c/strong\u003eThe authors declare no financial or non-financial interests exist that are directly or indirectly related to this work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declaration:\u0026nbsp;\u003c/strong\u003eThe authors confirm that all the research meets ethical guidelines.\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eGithinji, T. 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Assessment of groundwater sustainability in arid and semi-arid regions using a fuzzy Delphi method. \u003cem\u003eInternational Journal of Environmental Science and Technology\u003c/em\u003e, 1-22.\u003c/li\u003e\n\u003cli\u003eHowlader, R., Chowdhury, M. M. A., Jahan, C. S., Hossain, M. A., Rahaman, M. F., Ghose, B. K., \u0026amp; Islam, M. (2024). Delineation of fresh groundwater potentiality zones in saline coastal aquifers, Southwest Bangladesh using remote sensing and GIS approaches. \u003cem\u003eEnvironmental Geochemistry and Health\u003c/em\u003e, \u003cem\u003e46\u003c/em\u003e(11), 454.\u003c/li\u003e\n\u003cli\u003ePitchaimani, V. S., Joe, R. J., Shyamala, G., Manjula, G., Hemalatha, B., Babu, M. D., ... \u0026amp; Ravindran, G. (2024). Multivariate statistical and hydrogeochemical analysis of seasonal groundwater quality variations in coastal villages of Trivandrum district, south India. Discover \u003cem\u003eSustainability, 5(\u003c/em\u003e1), 1-32.\u003cstrong\u003e\u003c/strong\u003e\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Analytical Hierarchical Process, Artificial Groundwater Recharge Zones, Geospatial Techniques, Weighted Overlay Analysis","lastPublishedDoi":"10.21203/rs.3.rs-6331621/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6331621/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe groundwater extraction in coastal area is increased in recent year due to ecotourism and climate change, this results into depletion of groundwater level which needs the modelling and mapping for artificial groundwater recharge zones (AGRZ). The present study carried out at Mochemad River basin at western coastal part of Maharashtra India. The study area is having sea water intrusion as major problem can be resolved by the reaching runoff water as area receive more 3500mm rainfall. In view of this, the present study uses thematic layer for instance Lithology, Geomorphology, Land utilisation, Gradient, Drainage and Lineament Density and Rainfall with multicritical based Analytical Hierarchical Process analysis for giving accurate weights to each geospatial layer and its sub class. Further, the modelling of AGRZ was assessed with weighted overlay analysis in geospatial software. The finalised map of AGRZ is classified into four categories namely, unsuitable (14.24%), moderately suitable (19.25%), highly suitable (55.94%) and very highly suitable (10.56%). Finally, findings were validated using an assessment matrix of ROC (receiver operating characteristics) and AUC (area under curve), which revealed that the AHP approach performed reliably with an accuracy of 89%. Furthermore, various artificial recharge constructions like check dams, runoff infiltration tanks, Mati Bandara (soil bench trenching), and continuous contour trenching (CCT), are proposed in the demarcated favourable zones to facilitate the development, forecasting, and management of the water resources in the study area.\u003c/p\u003e","manuscriptTitle":"Modelling and Mapping of Artificial Groundwater Recharge Zones using Geospatial Techniques and Analytical Hierarchical Process (AHP) of Mochemad River Basin, Sindhudurg district, Maharashtra, India","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-13 10:41:11","doi":"10.21203/rs.3.rs-6331621/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"04002201-bc86-4b11-9443-18587b04c4a9","owner":[],"postedDate":"May 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-06-19T05:08:29+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-13 10:41:11","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6331621","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6331621","identity":"rs-6331621","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}