Optimizing Sustainable Landfill Sites in Rishikesh: Integrating Geospatial and MCDA for Waste Management in Himalayan Foothills

Optimizing Sustainable Landfill Sites in Rishikesh: Integrating Geospatial and MCDA for Waste Management in Himalayan Foothills | 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 Optimizing Sustainable Landfill Sites in Rishikesh: Integrating Geospatial and MCDA for Waste Management in Himalayan Foothills Jyoti Yadav, Anjali Naik, Vikas Rawat, Kamal Singh Bisht, M.S. Negi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7321392/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 Addressing the daily generation of approximately 80 metric tons of waste in Rishikesh, the Municipal Corporation faces significant challenges in solid waste management. Identifying adequate landfill sites is critical for authorities, and the purpose of this study is to identify the best areas for municipal solid waste disposal in Rishikesh. Geographic Information Systems (GIS) and Analytic Hierarchy Process (AHP) have been used as effective methods for Multiple Criteria Decision Analysis (MCDA) in waste management. Ten parameters, including distance from rivers, distance from road networks, lithological structure, elevation, soil texture, slope, and land use and land cover (LULC), normalized difference vegetation index (NDVI), distance to sensitive and restricted places and aquifers were measured for site analysis in all 40 wards of Rishikesh. The predictive maps were assessed using the receiver operating characteristic (ROC) method. A random selection of 8 potential landfill site locations was used, yielding an accuracy of 85.5% (AUC = 0.855) for the AHP model, demonstrating high reliability. The research site has been separated into various zones, designated as areas of very high, high, moderate, low, and extremely low appropriateness, accounting for 5%, 6.7%, 29.6%, 33.2%, and 25.2% of the total area, respectively. These zones were suitable for landfill purposes. By optimizing distances from the considered parameters, the study identified potential waste management sites. This information can assist urban planners and authorities in implementing successful urban waste management strategies. Physical Geography Geographic Information Systems Environmental Policy Analytic Hierarchy Process Geographic Information System Landfill Sites Municipal Solid Waste Multi Criteria Decision Analysis Himalayan Mountains Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1 Introduction India's rapid industrialization, population growth, and economic prosperity have resulted in a significant increase in municipal solid waste (MSW) output. Individuals migrate from rural areas to urban hubs seeking improved lives and social position, this phenomenon generates vast quantities of MSW daily, on an average of kilograms/capita/day (Kumar et al. 2020; Devi et al. 2020 ). Efficient MSW management, which depends on the waste composition, necessitates suitable waste management technologies. However, India faces the intricate challenge of achieving sustainable waste management because of the changing nature of waste kinds and shifting trash, generation at different sources. Insufficient waste management may lead to consequences on a local, regional, or even global scale, as demonstrated by the occurrences of climate change and environmental deterioration (Agamuthu et al. 2019 ). Effective waste management systems should incorporate landfills as a crucial aspect, acting as the ultimate location for municipal solid waste (MSW) after evaluating all available waste management options (Aljaradin and Persson 2021 ). However, improper disposal in landfills can lead to the generation of habitats for rodents, flies, and birds, causing chaos for local residents and environments (CPCB 2011 ). The decomposition of solid waste in these sites produces hazardous substances (MeBean et al. 2018 ; IPCC-AR5 2014), such as methane - a non-toxic yet highly combustible, explosive gas that ignites solid waste, contributing to air pollution (Abdul-Wahab 2020 ). Moreover, landfills emit harmful and unstable atmospheric contaminants, such as vinyl chloride (C2H3Cl) and tetrachloroethylene (C2Cl4), which pose health risks to people living near these sites (Shah 2017 ). Improper landfill methods can lead to water contamination by permitting leachate to permeate into groundwater sources, subsequently causing various health problems, including yellowing of the skin, feeling sick, breathing difficulties, pregnancy loss, and difficulties in conceiving (El-Fadel et al.2019). Incorporating sanitary landfilling within municipal solid waste management necessitates a comprehensive assessment of spatial data, considering numerous factors to determine a suitable waste disposal site (Dahake et al. 2024). Choosing an appropriate landfill location is vital to minimize environmental pollution. Nevertheless, implementing landfills has become more difficult due to community resistance and ecological worries. Since land is limited and valuable, the selection process for waste disposal must encompass diverse spatial, economic, and social aspects (Ghosh et al; 2020 ). To efficiently manage extensive data volumes, suitable technologies, such as satellite remote sensing (R.S.) data and Geographic Information System (GIS), are crucial for processing, analyzing, and handling both spatial and non-spatial data promptly (Ali & Ahmed; 2020). In complex decision-making situations featuring numerous interwoven themes and side-by-side evaluations, the Analytic Hierarchy Process (AHP) has proved its effectiveness as a valuable decision-making instrument. (Asefa et al; 2021 ). This research, which incorporates satellite data and hierarchy method, aims to identify the best location for garbage disposal within the research region. 2 Study Area Rishikesh, a quaint town in the Indian state of Uttarakhand, is a part of the Dehradun region. Nestled close to the majestic Himalayan mountain range, it is celebrated for its stunning landscape. Geographically, the town lies at 30.103368 degrees north latitude and 78.294754 degrees east longitude, in the Garhwal Himalayan Range foothills. Renowned as the 'Gateway to the Garhwal Himalayas' and the 'World Yoga Capital’, Rishikesh is located 340 meters above sea level.The climate of this town is defined as humid subtropical, the area typically experiences a peak temperature of 40°C, while the minimum temperature averages at 7°C. (Nagar Nigam Rishikesh; 2022). Covering an area of 26 square kilometres, Rishikesh municipality houses a population of 106,320 as per 2011 census, with 53% being males and 47% females. Approximately 21,300 households and 3,000 commercial establishments can be found within the town. Daily, the municipal corporation manages 80 metric tons of waste, with collection and segregation tasks handled by the local authorities (District Environment Plan of Dehradun; 2020 ). The administration of the area is divided into three regions, namely Dehradun, Pauri Garhwal and Tehri Garhwal. The scope of this research encompasses the Rishikesh Municipal Corporation area. To tackle the challenge of solid waste disposal, a 2 km buffer zone was designated around urban regions and neighbouring villages (Sujoy et al. 2024 ), creating a study area of 60.03 km², which includes 26 km² within the jurisdiction of the Rishikesh municipality. The Rishikesh Nagar Nigam (RNN) consists of 40 wards, evenly divided between residential and commercial zones as shown in Fig. 1. The central city area predominantly caters to commercial activities, featuring a high concentration of shops and businesses, while residential zones surround it (as mentioned in the Rishikesh Report of 2020) (Rishikesh Report; 2020). The municipality features residential and commercial zones. The city core exhibits a high concentration of shops and businesses, encircled by predominantly residential areas. A commercial area is also present near the railway station and alongside the River Ganga. The distribution of settlements throughout the urban region is relatively balanced. Population density per ward experiences minimal variations, with slightly denser concentrations in the city center. In 2016, it was estimated that 12,344 daily commuters and tourists visited (GIZ, 2020), which accounts for 12% of Rishikesh's overall population (Nagar Nigam Rishikesh; 2022). 3 Material and Methods 3.1 Data base The present research emphasises the use of Geographic Information Systems (GIS) in combination with the Analytic Hierarchy Process (AHP) to determine acceptable sanitary dump locations for the disposal of trash within the Rishikesh Municipal Corporation. For this investigation, GIS data pertaining to the area, including Slope, Lithological Structure, Land Use/Land Cover (LULC), Digital Elevation Model (DEM), Road Network, Distance from the River, Normalized Difference Vegetation Index (NDVI), Distance to Sensitive and Restricted Places, Soil Texture and Aquifers were gathered from several sources, including the Geological Survey of India (GSI), Central Ground Water Board (CGWB), Google Earth Pro, and Rishikesh Municipal Corporation as shown in Table 1 . The mapping information was processed and analysed within a Geographic Information System environment. The AHP methodically decomposes decision-making tasks into manageable components; each assessed individually and logically combined (Das et al. 2023 ). For site selection, ten criteria were chosen, drawing from the published literature and Municipal Solid Waste Management Rules (2016). Each criterion was evaluated using the rank technique. Least ranks indicate more suitable sites, while higher ranks suggest less desirable options. The parameters were ultimately integrated using the Weighted Overlay Method (WOM), and the outcomes were evaluated by determining the receiver operating characteristic (ROC) value and the area under the ROC curve (AUC). Table 1 The Outline of Thematic Layers Involved in Landfill Site Selection Criteria Data type Data sources Data details Altitude (m) Raster, 12.5 meters resolution NASA Earth Data (Alaska Satellite Facility) ALOS PALSAR Digital Elevation Model (DEM) Soil Texture Raster Layer India- WRIS IRS LISS-III and SRTM 30m Distance from the road (meters) Open Street Map, vector data, shape file Open Street Map Data Road network Lithological Structure Vector, Shape file Geological survey of India (GSI) Bhukosh- GSI Landuse/landcover (LULC) Raster Layer (Thematic), 10 meter resolution Global Esri Inc ESA Sentinal-2 Distance from the river (meters) Open Street Map, vector data, shape file Open Street Map Data River network Slope (degree) Raster, 12.5 meters resolution NASA Earth Data (Alaska Satellite Facility) ALOS PALSAR Digital Elevation Model (DEM) NDVI Raster, 5.8 meters Bhoonidhi, NRSC Resourcesat 2A LISS IV Distance from Sensitive Places Vector, Shapefile Google Earth Pro Satellite Image Aquifers Vector, Shapefile India-WRIS CGWB Data Source: Computed by author(s) All thematic layers were transformed into separate raster maps, following the methods of (Şener et al. (2011). The weights from AHP method were then computed Microsoft Excel. Employing Arc-GIS, essential geographical elements were extracted for analysis. These GIS sets of data were then processed and homogenised in terms of the projection system (WGS-1984) and uniform dimension of cells (refer to Fig. 3 ). 3.2 Criteria for Landfill Selection A landfill location must adhere to essential conditions to prevent groundwater and surface water contamination, as well as soil pollution. Additionally, considerations for settlements and infrastructure are crucial for public health. Proximity to existing roads reduces transportation costs (Thulasi et al. 2024 ). This study employs ten criteria for evaluating landfill suitability, drawing from Indian standards, regulations, and published literature. The methodology involves separate maps for each criterion, culminating in a final composite map through Weighed Overlay Analysis (WOA). The following sections examine these landfill site selection factors in more detail, as depicted in the Fig. 3 . 3.2.1 Land Elevation Land elevation significantly impacts landfill site construction and operation, as a negative correlation exists between site suitability and elevation height. To account for this, an elevation map was created using ArcGIS tools (Khan et al. 2023). The elevation of the research region is between 267 to 909 metres above mean sea level (MSL). Three distinct buffer zones were established (refer to Fig. 2a) and given weight values according to how suitable their elevation is for choosing a dump site. Areas with elevations below 300 meters were deemed highly suitable, those between 301 and 500 meters were considered moderately suitable, and regions with elevations above 501 meters were classified as least suitable and less preferable for landfill construction (see Table 2 ). 3.2.2 Slope Morphology of land in any place is measured using slope gradient that is normally expressed as a percentage or as an angle (Thulasi et al. 2024 ). A landfill construction would not be technically feasible on areas with steeper slopes because this leads to leachate migration besides Soil and pollution of water (Olorunlana et al. 2022; Ali et al. 2020 ) and unsuitable from an economic standpoint to build landfills (Premsudha et al. 2022 ). The slope layer map was created using Arc GIS and SRTM DEM data for the study region. Terrain of the region has a ranging gradient from 20° that was later reclassified into steep (> 20°), moderate (10°- 20°) and plane (< 10°) areas assigned weightage from 0–1 respectively (Table 2 ). Areas with weightage of 1 were considered as the most suitable since they had slope < 10° which is highly favourable for sanitary landfills (Fig. 2b) while those with slopes over 20 ̊ were deemed unsuitable for landfilling operations. 3.2.3 Lithological Structure It is vital to consider the lithological structure while selecting a landfill location. The explanation for the lithological structure is according to Geological Survey of India (GSI), India. One way geology influences this is by controlling infiltration rate and low infiltration formation is considered desirable for garbage disposal because it prevents pollutants from leaking from the site of disposal to ground water. The ranking for this sub-criterion can be seen on Table 2 . Nine lithological formations are found in the study area (Fig. 2c). Landfill development in the study area should prioritize zones weighted 0.75 to 1, with a focus on lithology rich in clay or shale which ensures low permeability and good containment, suitable for landfill site (Majid & Mir 2021 ). Zones weighted 0.5 may be considered with additional engineering safeguards and zones weighted 0- 0.25 are least or unsuitable due high permeability or poor containment characteristics which can cause high environmental risks and should be avoided. 3.2.4 Soil Texture The soil texture of study area is primarily divided into three regions as per India-WRIS, Ministry of Jal Shakti illustrated in Fig. 2d. Soil with fine particles (e.g., clay or silty clay) are ideal for landfills because they offer low permeability and act as a natural barrier, reducing the risk of leachate seepage into groundwater (Alkaradaghi et al. 2019 ) so are given high weightage i.e., 1. On the other hand, rocky and non-soil are least or unsuitable for landfills as they are highly permeable and lack the structural and chemical properties to retain contaminants or support landfill infrastructure effectively so are given least weightage i.e., 0.25 (as shown in Table 2 ). 3.2.5 Landuse and Landcover (LULC) In this research, the Landuse and Landcover (LULC) map encompasses regions such as built-up areas, water bodies, forest cover, agricultural land, rangeland, and bare ground. High resolution data (Sentinel-2 having a 10-meter resolution) is employed for classifying land usage. From an economic standpoint, bare ground and rangeland are deemed the most suitable options for a landfill site, as they can be sold post-landfill completion and face less public resistance (Suchitra 2018 ). In the study area, bare ground and rangeland are assigned the highest suitability weightage i.e., 1 for establishing a landfill site (Table 2 ), while water bodies and built-up regions are determined undesirable due to leachate contamination, ecological sensitivity and habitat destruction, so are given least weightage. Agricultural land are assigned as moderate suitable because landfills could affect soil fertility and probable detrimental effects on residential areas, such as odour, noise and dust (Kosoe et al. 2021 ). The suitability is visually represented in Fig. 2e. 3.2.6 Distance from Roads In this research, the choice of feasible locations for landfills takes into consideration the closeness to roadways, because rising construction and transportation expenses arise with increased gaps among garbage generating locations and potential dumping areas (Das and Bhattacharyya 2015 ; Guler and Yomralioglu 2017 ). While certain studies suggest locating waste disposal sites at a greater distance from road systems to address aesthetic and ecological concerns (Kalisha et al. 2024). In such cases, maintaining a certain distance from roads can reduce transportation costs. In order to tackle these elements, two ring buffers (less than 100 meters and more than 100 meters) are established around the road system (Fig. 2f). Each buffer zone was then given a weightage according to specific placement criteria (Table 2 ). 3.2.7 Distance from the River In India, there is a legal restriction preventing the disposal of solid trash in or near any water surface, including rivers or lakes (CPCB, 2008). Consequently, maximum distance from any water body holds significant weight in selection of site, while minimum distance canals to rivers is deemed inappropriate for location choice (CPHEEO, 2016 ). In this study, area under the buffer zone of 500 meters is deemed least or unsuitable for landfill construction and is weighted 0.25, also the area more than 1000 meters from the river is weighted as the most suitable i.e., 1 (Table 2 ). The suitability map based on proximity to rivers is demonstrated through Fig. 2g. 3.2.8 Normalized Difference Vegetation Index (NDVI) Vegetation cover acts as a natural barrier, minimizing the spread of contaminants through wind (Lin et al., 2020 ). To analyze vegetation's influence on aerial dispersion, the NDVI (Normalized Difference Vegetation Index) was utilized, as it effectively represents vegetation's impact on contaminant movement (Duarte et al., 2014 ). This index enables the identification, classification, and estimation of vegetation or biomass in a given area (Hamimina et al., 2013). In this research, NDVI values were derived using satellite imagery from Resourcesat 2A LISS IV. A threshold value of 0.5 is applied for feature delineation. The NDVI map ranges from less than 0.5 to more than 0.5, where values greater than 0.5 signify healthy vegetation, values near 0 indicate a lack of vegetation, and values approaching lower than 0.5 represent water bodies. On the NDVI map, vegetated regions are visually distinguished by a green colour (Fig. 2h). 3.2.9 Distance from Sensitive and Restricted Places According to the Indian Municipal Solid Waste Management Rules of 2016, sanitary landfill sites must not be located near sensitive areas such as children's parks, natural parks, offices, banks, or critical habitats and eco-fragile zones (Yadav et al. 2024 ). As an expanding urban area, it is crucial to account for this guideline, ensuring that landfill sites are not established close to restricted or sensitive locations (Kontos et al. 2005 ; Guler and Yomralioglu 2017 ). This study recommends that areas within 100 meters of sensitive and restricted zones, including schools, colleges, banks, railway stations, children's parks, and offices, should be deemed unsuitable for landfill sites (Ali et al. 2020 ). Instead, locations at a greater distance should be selected for such purposes (Fig. 2i). 3.2.10 Aquifers Aquifers play a vital role in assessing the suitability of landfill sites as they are integral to the groundwater system. Landfills produce leachate, a potentially hazardous liquid generated when water filters through waste, which can contaminate aquifers without adequate safeguards (Vasoogh et al. 2017). In the study area, three primary aquifers rocks are identified: limestone/dolomite, older alluvium (comprising silt, sand, gravel, and lithomargic clay), and pebble/gravel/bazada/kandi. Aquifers with high permeability, such as gravel or karstic limestone/ dolomite, facilitate rapid leachate migration, posing a significant risk of contamination (Ivana et al. 2021 ) and therefore receive lower weightage i.e., 0. Conversely, older alluvium aquifers with low permeability are more effective in containing leachate, making them more favourable, hence given high weightage i.e., 1 (refer Table 2 ). Implementing protective measures, such as liners and leachate collection systems, is crucial to ensure environmentally sustainable landfill sites (Fig. 2j). Table 2 Overview of Rankings and Suitability Levels Used in Choosing a Potential Landfill Site in the Study Area Criteria Sub- criteria/alternatives Suitability index (Weightage) Level of Suitability Total Area (in %) References Distance to roads (m) > 100 1 High Suitable 92.762 Ali et al. ( 2020 ), 1000 1 High suitable 58.617 Kosoe et al. ( 2021 ) 500–1000 0.5 Moderate Suitable 15.532 Elmo et al. (2023) < 500 0 Unsuitable 25.850 Pasalari et al. ( 2019 ) Lithological Structure Shale with Lenticles of Limestone 1 Very High suitable 0.173 Premsudha et al. ( 2022 ) Grey Sand, Silt and Clay 1 Very High suitable 11.772 Şener et al. (2011) Silt, Clay, Sand with Gravel and Pebbles 0.75 High suitable 54.496 Grey Micaceous Sand, Silt and Clay 0.75 High suitable 24.018 Argillaceous Limestone and Clay 0.5 Moderate suitable 0.429 Limestone, Dolomitic Limestone with Shale 0.5 Moderate suitable 0.283 Diamictite, Quartzite, Slate and Boulder Bed 0.5 Moderate suitable 6.255 Carbonaceous Shale, Slate, Greywacke 0.25 Low suitable 0.403 Massive Sandy Limestone 0 Unsuitable 2.165 Slope ( ̊ ) 20 0 Unsuitable 7.733 Ali and Ahmad ( 2020 ), Elevation (m) 267–300 1 High suitable 28.736 Khan et al. (2023); 301–500 0.5 Moderate suitable 65.781 Şener et al. (2010, 2011), 501–909 0.25 Low Suitable 5.482 Soil Texture Fine Texture 1 High Suitable 32.693 Ali et al. ( 2020 ); Adar et al. (2023) Medium Texture 0.5 Moderate Suitable 24.367 Kapilan and Elangovan ( 2018 ); Paul et al. ( 2014 ) Rocky and Non- Soil 0.25 Low Suitable 42.938 (Alkaradaghi et al. 2019 ) Landuse/Landcover (LULC) Bareground 1 High suitable 9.519 Upadana et al. ( 2023 ) Rangeland 1 High Suitable 2.231 Thulasi et al. ( 2024 ) Agricultural Land 0.5 Moderate suitable 1.732 Ali and Ahmad ( 2020 ) Forest Cover 0.25 Low Suitable 46.341 Water Bodies 0 Unsuitable 4.943 Built-up Areas 0 Unsuitable 35.231 NDVI > 0.5 1 High suitable 81.767 Lin et al., ( 2020 ) 301 1 High suitable 94.558 Yadav et al., ( 2024 ) Places (m) 201–300 0.5 Moderate Suitable 2.815 Kontos et al., ( 2005 ) < 200 0 Unsuitable 2.625 Guler and Yomralioglu ( 2017 ) Aquifers Older Alluvium (silt, sand, gravel, and lithomargic clay) 1 High suitable 58.413 Vasoogh et al. 2017 Pebble/Gravel/Bazada/Kandi 0.25 Moderate Suitable 18.275 Ivana et al. ( 2021 ) Limestone/Dolomite 0 Unsuitable 23.310 Source: Computed by the author(s) 3.3 Analytic Hierarchy Process (AHP): A Multicriteria Technique AHP involves organizing the chosen criteria into a system of hierarchy based on the overarching objective (Kosoe et al. 2021 ). In practice, this technique consists of numerous stages, including building a decision structure for the criteria, calculating their relative relevance, analysing priorities for each criterion, deriving ultimate objectives, and assessing the decision's sensitivity (Paul et al. 2024 ; Saaty, 1980 ). However, in practice, AHP's primary limitation is the use of a precise numerical value, which may be insufficient due to subjectivity in human perspectives and rating for comparison matrices (Ali and Ahmad, 2020 ). Despite this, AHP effectively addresses complex decision-making scenarios in real-life situations and often provides better outcomes compared to other MCDM techniques, particularly when integrated with geographical information systems and spatial data with geographic information system and spatial data (Ali and Ahmad 2019a , b ). Within the scope of this research, the Analytic Hierarchy Process was utilized as a process to develop decisions based on multiple criteria, working alongside Geographic Information Systems (GIS) were used to efficiently select the best dump location for the Rishikesh Municipal Corporation. All criteria and sub criteria were given weightage and ranking according to their level of suitability as illustrated in Table 2 and Fig. 4 presenting the pair wise correlation between these parameters. In order to determine the significance of the selected criteria, the research utilized numerical ratings derived from Saaty's Pair-wise Comparison Method, which spans between 1 and 9. 3.3.1 Calculating weight normalisation: To generate a normalized pair-wise comparison matrix, the following equation is used: $$\:NW=GM\frac{GM}{\:{Ʃ}^{N\:}n-1}{GM}_{n}$$ Where, NW refers to the calculation of Normalized weights, while GMn stands for the geometric mean computation of the nth row in Px. Table 3 Criteria Weight of Ten Thematic Aspects for Efficient Landfill Site Determination Criteria Criteria Weights Rank DR 0.10 5 DRV 0.11 2 LS 0.07 10 SL 0.09 8 LE 0.10 5 ST 0.10 5 LULC 0.13 1 NDVI 0.11 2 DSP 0.11 2 AQ 0.08 9 (DR distance to roads, DRV distance to river, LS lithological structure, SL slope, LE land elevation, ST soil texture, LULC land-use and land-cover, NDVI normalized difference vegetation index, DSP distance to sensitive places, AQ aquifers) Source: Computed by author(s) 3.3.2 Consistency Index (CI) After calculating weights and providing ranking to each parameter (Table 3 ) we have to calculate consistency index. The Consistency Index formula in Saaty's analytic hierarchy process approach is explained as: $$\:CI=\frac{({\lambda\:}_{max}-n)}{n-1}$$ The term 'λ max' refers to the eigenvalue of the comparison matrix. The given equation explains that the product of Px with the weight of each criterion creates the consistency vector, which in this case is 9.564. N represents the total number of elements involved. The Consistency Index (0.070), as calculated, is presented in Table 4 . To determine Consistency Ratio (CR), it is necessary to use this CI value. 3.3.3 Consistency ratio (CR) For ensuring the significance of the derived weight, it is crucial to assess the consistency of the results. For this, we need to compute the value of Consistency Ratio (CR) of the Paired Comparison Matrix (PCM). If the level of consistent ratio is greater than 0.1 or 10%, the decision-making process is deemed inconsistent and must be repeated. Conversely, a CR of 0 implies a perfectly consistent decision. The formula of CR: CR = \(\:\frac{\varvec{C}\varvec{I}}{\varvec{R}\varvec{I}}\) In this formula, The Consistency Ratio is denoted as CR, the Consistency Index as CI, and the Random Index as RI. To calculate the Random Index (RI), we used Saaty's index table. Table 4 The outcome of the consistency check for all thematic aspects combined in the landfill suitability process λ max N RI CI CR Consistency 9.56 10 1.49 0.07 0.05 CR < 0.1(Yes) Source: Computed by the author(s) The RI value for the ten parameters remains constant at 1.49. The assessment, having a meticulously calculated Consistency Ratio (CR) of 0.05, is considered entirely consistent. Consequently, AHP pair-wise matrix's judgment is deemed valid for selecting suitable waste management sites within the Rishikesh Municipal Corporation. 4 Results and Discussion In this research, a set of ten parameters, encompassing environmental and economic aspects relevant to the study area's issues, were established. These criteria were mapped using GIS and assigned weights according to the AHP methodology. Evaluation of these criteria adhered to the MSWM Rules, 2016, India, and relevant literature. A matrix for comparing pairs was created to evaluate the significance of each thematic layer, leading to an acceptable consistency ratio (CR at 0.05). Suitability weights for each parameter, indicate that the parameter of land use and land cover, distance from river, sensitive places and normalized difference vegetation index have been assigned to the highest values at 0.13 and 0.11, respectively, while Lithological Structure, aquifers and slope have the lowest values at 0.07, 0.08 and 0.09, respectively, among the ten essential criteria, as shown in Table 3 . Importance given to the parameter of land use and land cover in Rishikesh stems from its status as a rapidly developing city in Uttarakhand, known for its popularity as a tourist destination and the global Yoga capital. As a result, selecting a site on the city's outskirts with a lower population density and settlement than the central area is recommended. Furthermore, the distance from the Ganga River is highlighted because landfill sites can lead to water and soil pollution, potentially harming human health. The pollution of the Ganga River along its course through the plains, caused by nearby dump sites, makes these locations unsuitable. The lowest weightage is assigned to lithological structure, aquifers, and slope because the areas deemed unsuitable are located along the river or predominantly on its eastern and north eastern sides, which have already been classified as unsuitable due to their proximity to the river. Table 5 Level of Suitability, Suitable Areas, and the Percentage of Total Area Coverage Suitability Levels Weightage Area (sq.km) Total Area Coverage (in %) Very High Suitable 1 3.503 5.066 High Suitable 0.75 4.683 6.773 Moderate Suitable 0.5 20.514 29.666 Low Suitable 0.25 23.013 33.279 Unsuitable 0 17.435 25.213 Source: Computed by the author(s) Land-use and land-cover have been given higher emphasis in the suitability assessment, as it should avoid placing landfills near populated zones, and is crucial to keep them distant from urban developments, thriving greenery, water sources, and fertile farming regions, particularly in the area under investigation. Among these, bare ground and rangeland have been identified as the most suitable locations for landfills. Distance from roads and slope are also crucial factors in determining weight scores. The GIS environment merges the criteria and sub-criteria weights into thematic layers, generating a map of landfill suitability. The map is categorized into five groups: Very High Suitable, High Suitable, Moderate Suitable, Low Suitable and Unsuitable as illustrated in Fig. 5 . According to the map, it is indicated that 6.7% of the Rishikesh Municipal Corporation region is appropriate, 29.6% can be considered moderately suitable, 33.2% has lesser suitability, 25.2% is deemed unsuitable and only 5% area is highly appropriate for making a suitable landfill site region (refer to Table 5 ). Ideal landfill locations should be in areas characterized by low elevation, minimal slope, considerable distance from roads and rivers, and a limited presence of residences. These factors contribute to the overall suitability of the site for landfill purposes. 4.1 Model Evaluation and Validation The predictive performance of the final suitability map generated using AHP was evaluated using the receiver operating characteristic (ROC) value and the area under the ROC curve (AUC). The ROC curve is a graphical representation that illustrates binary classification performance by varying a threshold. It plots the true positive rate (TPR), or sensitivity, against the false positive rate (FPR), which is calculated as 1-specificity (Nandi et al., 2010). An ideal ROC curve approaches the top-left corner of the plot (Fig. 6 ). To better assess the ROC curve and model accuracy, the AUC score is calculated, ranging from 0.5 (random guessing) to 1 (perfect fit) (Fawcett, 2006 ). In this study, since the region is considered small, the number of potential sites is relatively low, the AUC-ROC curve was used with a validation dataset of 8 GPS points obtained through Google Earth imagery and area visits to assess the accuracy of AHP in landfill site suitability mapping. The landfill suitability map was quantitatively validated by computing the AUC, resulting in a value of 0.855 or 85.5%, which lies within the good range of 0.8 to 1, indicating highly reliable results (Kamdar et al., 2019 ). High-resolution Google Earth images and study field survey further confirmed that the identified suitable areas primarily consisted of open or bare land, demonstrating the model's strong predictive capability in pinpointing these locations (Fig. 7 ). Six primary candidate locations were identified, with the fourth site aligning with the government-proposed location for a scientific landfill at Lalpani Beet No-1, Rishikesh. This site is designated for waste treatment and includes facilities for RDF (Refuse-Derived Fuel), composting, and secured landfill. The plant will have a capacity of 240 TPD (tons per day) and will also feature a leachate treatment plant. This alignment validates the site selection process, demonstrating that the ROC-AUC method is highly effective for the study region. 5 Conclusion and Suggestion The rapid population growth and evolving lifestyle in Rishikesh have resulted in a substantial rise in Municipal Solid Waste (MSW), causing challenges in efficient management by the local authorities due to rising land costs, limited budgets, and traditional waste disposal practices. In the presented research, a site suitability model that incorporates Geographic Information Systems (GIS) can aid professionals such as researchers, urban designers, civil engineers, decision-makers and officials are involved in determining the most acceptable places for building waste disposal facilities. This approach aims to maintain waste management sustainability and safeguard public health from potential hazards like atmospheric impurity, water contamination, unpleasant scents, and hazardous gas releases from waste combustion. As a tourist destination, Rishikesh already faces public health issues due to traffic-related CO2, NO2, and SO2 emissions, as well as increased waste generation from the floating population. This research presents a swift, evidence-backed approach for making decisions on MSW disposal, enhancing public health and fostering ecological sustainability. The study collected and systematized pertinent information related to particular criteria within a Geographic Information System (GIS) setting. The Weighted Overlay Analysis (WOA), a widely accepted method, was employed to create a suitability map based on the collected information. The Analytical Hierarchy Process (AHP) was employed to allocate importance and execute an overlay analysis for the most suitable location choice. This approach not only identified areas with potential environmental risks but also excluded them as unsuitable options for site selection. In cities like Rishikesh, which attract pilgrims, there is a notable disparity between the generated waste and waste disposal. Among the 40 wards in the city, 20 wards have a predominantly rural setting, where the input of wet waste into the collection system is comparatively low. This is due to the practice of feeding organic residues to animals or repurposing waste flowers for incense production. Consequently, a substantial portion of organic waste remains excluded from the urban waste collection system. In the Landfill Site Suitability Map, a significant portion of the city center is deemed unsuitable or least suitable for waste disposal facilities. This is attributed to factors such as densely populated area, allocation of an urban center, expensive prices of land, the proximity to a continuously flowing river, and the influence of geomorphic and lithological characteristics. The 500-meter-wide stretch alongside the Ganga River is entirely unsuitable for landfills, despite having a considerably higher waste generation rate compared to peripheral areas. This is due to the existing waste disposal site in Govind Nagar ward, which falls within a 500-meter zone from the riverbank. Rishikesh currently lacks adequate scientific facilities for processing, sorting, or treating dry waste. Consequently, both dry and majority of wet waste from residential, commercial, institutional, hotel, and other waste generation sources are collected and transported to the dumping ground situated in Govind Nagar. The waste disposal site, spanning around 6.5 hectares, is an open area without any scientific waste management provisions. Due to its unplanned layout, it lacks a designated capacity limit. Despite the site's central location within the city and proximity to residential areas, expanding it is not viable. Consequently, waste accumulation has led to an increase in the dumping ground's height. This fluctuating elevation makes it difficult to accurately measure the volume of waste disposed of. In terms of environmental considerations, the decision has been made to avoid choosing landfill sites near surface water sources. Consequently, the primary factors taken into account when choosing options were economic and environmental considerations. The optimal spots for managing waste disposal predominantly lie within the south and south-western regions of the research zone as demonstrated in Fig. 5 , covering approximately 5% of the region. In the post-processing evaluation, the appropriate location was re-examined, and ground verification was conducted to ascertain if all ecological and social criteria were met. Additionally, the final acceptable and unacceptable zones were reviewed with the municipal department, confirming the selected sites. They also mentioned that a new landfill site proposal had been submitted to the Ministry of Environment, situated within the suitability zone of the map results, specifically in the south-western region. Consequently, the findings support the new proposed landfill site for Rishikesh Municipal Corporation, highlighting its relevance and consistency. In summary, this research suggests implementing its findings to address landfill site selection challenges in Rishikesh city and other rapidly urbanizing areas worldwide that confront similar issues. Declarations Conflict of interest: The authors declare no conflict of interest. Data Availability Statement The datasets analyzed during this study are derived from publicly accessible sources, Geological data is available at the Bhukosh Portal () by Geological Survey of India (GSI), Soil texture and Aquifer type data is available at Water Resource Information System (WRIS) (), ALOS DEM has been downloaded from NASA Earth Data (), LULC from Esri Inc. (), and the high resolution Resourcesat-2A LISS IV Satellite images from Bhoonidhi (). The study area shape file have been obtained from Rishikesh Municipal Corporation office. GCPs have been taken using Garmin GPS. References Abdul-Wahab, S. A. (2020). Modelling methane and vinyl chloride in soil surrounding landfill. International Journal of Environmental Pollution, 21, 339–349 https://doi.org/10.1007/s42489-020-00052-1 Adar, E., Adar, T. (2023). Comprehensive Evaluation of Hazardous Solid Waste Treatment and Disposal Technologies by a New Integrated AHP&MARCOS Approach. 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Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7321392","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":497333049,"identity":"f7340bc0-b890-41a4-ae0c-a8e0b2117736","order_by":0,"name":"Jyoti Yadav","email":"","orcid":"https://orcid.org/0009-0001-7560-5990","institution":"Department of Geography, D.B.S. (P.G.) 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study area.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSource: Department of Municipal Corporation, Rishikesh\u003c/em\u003e\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7321392/v1/f264aa0b892ed44691a9df9e.jpg"},{"id":88614660,"identity":"12484c61-bfa1-4055-854d-78335ec96cfd","added_by":"auto","created_at":"2025-08-08 10:24:07","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":458954,"visible":true,"origin":"","legend":"\u003cp\u003ePresenting the Landfill Site Identification Framework and Key Influencing Factors \u003cstrong\u003ea) \u003c/strong\u003eElevation \u003cstrong\u003eb) \u003c/strong\u003eSlope,\u003cstrong\u003e c) \u003c/strong\u003eLithological Structure,\u003cstrong\u003e d) \u003c/strong\u003eSoil Texture,\u003cstrong\u003e e) \u003c/strong\u003eLand use and Land cover (LULC),\u003cstrong\u003e f) \u003c/strong\u003eDistance from Roads, \u003cstrong\u003eg) \u003c/strong\u003eDistance from River,\u003cstrong\u003e h) \u003c/strong\u003eNormalized Difference Vegetation Index (NDVI),\u003cstrong\u003e i) \u003c/strong\u003eDistance from Sensitive Places and\u003cstrong\u003e j) \u003c/strong\u003eAquifers\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSource: Computed by author(s)\u003c/em\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7321392/v1/82f816a57bc7921ede704491.png"},{"id":88614964,"identity":"8aeb1a5a-666b-4dd4-be91-36b7128d414b","added_by":"auto","created_at":"2025-08-08 10:32:07","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":153974,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart illustrating the methodology adopted for this study.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7321392/v1/19eacb6ccab1bef267dfccb6.jpg"},{"id":88614965,"identity":"32cfca32-e66e-49c9-b8ff-2b8dd947d642","added_by":"auto","created_at":"2025-08-08 10:32:07","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":44289,"visible":true,"origin":"","legend":"\u003cp\u003eHeat map illustrating the relationship between each parameter utilized in the AHP-MCDA methodology.\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7321392/v1/5955b3132c8ece2d83543019.jpg"},{"id":88614665,"identity":"191f65e5-97fa-4497-a72b-f2b50595fa97","added_by":"auto","created_at":"2025-08-08 10:24:08","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":150870,"visible":true,"origin":"","legend":"\u003cp\u003ePotential landfill site zones of the study area\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7321392/v1/38280d9c73b0e89e4c6b3372.jpg"},{"id":88614674,"identity":"c5694bff-c7bf-4a4a-a5e0-e337bb28b4ef","added_by":"auto","created_at":"2025-08-08 10:24:08","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":35209,"visible":true,"origin":"","legend":"\u003cp\u003eROC curve and AUC score for the AHP model\u003c/p\u003e","description":"","filename":"6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7321392/v1/6d889f374a8eba322d470df6.jpg"},{"id":88614685,"identity":"ed7f6548-a9a1-4257-b80c-3d491afe3a3c","added_by":"auto","created_at":"2025-08-08 10:24:08","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":199013,"visible":true,"origin":"","legend":"\u003cp\u003eMap showing landfill site suitability (LSS) along with 6 top-priority candidate locations identified through post-processing analysis and field verification.\u003c/p\u003e","description":"","filename":"7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7321392/v1/4526262a547ec789c437a91d.jpg"},{"id":88616133,"identity":"f861124b-c4c3-43a4-8102-bca4af0f5c0d","added_by":"auto","created_at":"2025-08-08 10:48:09","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2366663,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7321392/v1/db5511c8-d8ae-48e1-abd6-d451957e44e6.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eOptimizing Sustainable Landfill Sites in Rishikesh: Integrating Geospatial and MCDA for Waste Management in Himalayan Foothills\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eIndia's rapid industrialization, population growth, and economic prosperity have resulted in a significant increase in municipal solid waste (MSW) output. Individuals migrate from rural areas to urban hubs seeking improved lives and social position, this phenomenon generates vast quantities of MSW daily, on an average of kilograms/capita/day (Kumar et al. 2020; Devi et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Efficient MSW management, which depends on the waste composition, necessitates suitable waste management technologies. However, India faces the intricate challenge of achieving sustainable waste management because of the changing nature of waste kinds and shifting trash, generation at different sources. Insufficient waste management may lead to consequences on a local, regional, or even global scale, as demonstrated by the occurrences of climate change and environmental deterioration (Agamuthu et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eEffective waste management systems should incorporate landfills as a crucial aspect, acting as the ultimate location for municipal solid waste (MSW) after evaluating all available waste management options (Aljaradin and Persson \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). However, improper disposal in landfills can lead to the generation of habitats for rodents, flies, and birds, causing chaos for local residents and environments (CPCB \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). The decomposition of solid waste in these sites produces hazardous substances (MeBean et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; IPCC-AR5 2014), such as methane - a non-toxic yet highly combustible, explosive gas that ignites solid waste, contributing to air pollution (Abdul-Wahab \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Moreover, landfills emit harmful and unstable atmospheric contaminants, such as vinyl chloride (C2H3Cl) and tetrachloroethylene (C2Cl4), which pose health risks to people living near these sites (Shah \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Improper landfill methods can lead to water contamination by permitting leachate to permeate into groundwater sources, subsequently causing various health problems, including yellowing of the skin, feeling sick, breathing difficulties, pregnancy loss, and difficulties in conceiving (El-Fadel et al.2019).\u003c/p\u003e\u003cp\u003eIncorporating sanitary landfilling within municipal solid waste management necessitates a comprehensive assessment of spatial data, considering numerous factors to determine a suitable waste disposal site (Dahake et al. 2024). Choosing an appropriate landfill location is vital to minimize environmental pollution. Nevertheless, implementing landfills has become more difficult due to community resistance and ecological worries. Since land is limited and valuable, the selection process for waste disposal must encompass diverse spatial, economic, and social aspects (Ghosh et al; \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). To efficiently manage extensive data volumes, suitable technologies, such as satellite remote sensing (R.S.) data and Geographic Information System (GIS), are crucial for processing, analyzing, and handling both spatial and non-spatial data promptly (Ali \u0026amp; Ahmed; 2020). In complex decision-making situations featuring numerous interwoven themes and side-by-side evaluations, the Analytic Hierarchy Process (AHP) has proved its effectiveness as a valuable decision-making instrument. (Asefa et al; \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This research, which incorporates satellite data and hierarchy method, aims to identify the best location for garbage disposal within the research region.\u003c/p\u003e"},{"header":"2 Study Area","content":"\u003cp\u003eRishikesh, a quaint town in the Indian state of Uttarakhand, is a part of the Dehradun region. Nestled close to the majestic Himalayan mountain range, it is celebrated for its stunning landscape. Geographically, the town lies at 30.103368 degrees north latitude and 78.294754 degrees east longitude, in the Garhwal Himalayan Range foothills. Renowned as the \u0026apos;Gateway to the Garhwal Himalayas\u0026apos; and the \u0026apos;World Yoga Capital\u0026rsquo;, Rishikesh is located 340 meters above sea level.The climate of this town is defined as humid subtropical, the area typically experiences a peak temperature of 40\u0026deg;C, while the minimum temperature averages at 7\u0026deg;C. (Nagar Nigam Rishikesh; 2022). Covering an area of 26 square kilometres, Rishikesh municipality houses a population of 106,320 as per 2011 census, with 53% being males and 47% females. Approximately 21,300 households and 3,000 commercial establishments can be found within the town. Daily, the municipal corporation manages 80 metric tons of waste, with collection and segregation tasks handled by the local authorities (District Environment Plan of Dehradun; \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). The administration of the area is divided into three regions, namely Dehradun, Pauri Garhwal and Tehri Garhwal. The scope of this research encompasses the Rishikesh Municipal Corporation area. To tackle the challenge of solid waste disposal, a 2 km buffer zone was designated around urban regions and neighbouring villages (Sujoy et al. \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e), creating a study area of 60.03 km\u0026sup2;, which includes 26 km\u0026sup2; within the jurisdiction of the Rishikesh municipality. The Rishikesh Nagar Nigam (RNN) consists of 40 wards, evenly divided between residential and commercial zones as shown in Fig. 1. The central city area predominantly caters to commercial activities, featuring a high concentration of shops and businesses, while residential zones surround it (as mentioned in the Rishikesh Report of 2020) (Rishikesh Report; 2020). The municipality features residential and commercial zones. The city core exhibits a high concentration of shops and businesses, encircled by predominantly residential areas. A commercial area is also present near the railway station and alongside the River Ganga. The distribution of settlements throughout the urban region is relatively balanced. Population density per ward experiences minimal variations, with slightly denser concentrations in the city center. In 2016, it was estimated that 12,344 daily commuters and tourists visited (GIZ, 2020), which accounts for 12% of Rishikesh\u0026apos;s overall population (Nagar Nigam Rishikesh; 2022).\u003c/p\u003e"},{"header":"3 Material and Methods","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Data base\u003c/h2\u003e\u003cp\u003eThe present research emphasises the use of Geographic Information Systems (GIS) in combination with the Analytic Hierarchy Process (AHP) to determine acceptable sanitary dump locations for the disposal of trash within the Rishikesh Municipal Corporation. For this investigation, GIS data pertaining to the area, including Slope, Lithological Structure, Land Use/Land Cover (LULC), Digital Elevation Model (DEM), Road Network, Distance from the River, Normalized Difference Vegetation Index (NDVI), Distance to Sensitive and Restricted Places, Soil Texture and Aquifers were gathered from several sources, including the Geological Survey of India (GSI), Central Ground Water Board (CGWB), Google Earth Pro, and Rishikesh Municipal Corporation as shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The mapping information was processed and analysed within a Geographic Information System environment. The AHP methodically decomposes decision-making tasks into manageable components; each assessed individually and logically combined (Das et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). For site selection, ten criteria were chosen, drawing from the published literature and Municipal Solid Waste Management Rules (2016). Each criterion was evaluated using the rank technique. Least ranks indicate more suitable sites, while higher ranks suggest less desirable options. The parameters were ultimately integrated using the Weighted Overlay Method (WOM), and the outcomes were evaluated by determining the receiver operating characteristic (ROC) value and the area under the ROC curve (AUC).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe Outline of Thematic Layers Involved in Landfill Site Selection\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCriteria\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eData type\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eData sources\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eData details\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAltitude (m)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRaster, 12.5 meters resolution\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNASA Earth Data (Alaska Satellite Facility)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eALOS PALSAR Digital Elevation Model (DEM)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSoil Texture\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRaster Layer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIndia- WRIS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIRS LISS-III and SRTM 30m\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDistance from the road (meters)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOpen Street Map, vector data, shape file\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOpen Street Map Data\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRoad network\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLithological Structure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVector, Shape file\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGeological survey of India (GSI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eBhukosh- GSI\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLanduse/landcover (LULC)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRaster Layer (Thematic), 10 meter resolution\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGlobal Esri Inc\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eESA Sentinal-2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDistance from the river (meters)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOpen Street Map, vector data, shape file\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOpen Street Map Data\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRiver network\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSlope (degree)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRaster, 12.5 meters resolution\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNASA Earth Data (Alaska Satellite Facility)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eALOS PALSAR Digital Elevation Model (DEM)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNDVI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRaster, 5.8 meters\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBhoonidhi, NRSC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eResourcesat 2A LISS IV\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDistance from Sensitive Places\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVector, Shapefile\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGoogle Earth Pro\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSatellite Image\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAquifers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVector, Shapefile\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIndia-WRIS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCGWB Data\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cem\u003eSource: Computed by author(s)\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eAll thematic layers were transformed into separate raster maps, following the methods of (Şener et al. (2011). The weights from AHP method were then computed Microsoft Excel. Employing Arc-GIS, essential geographical elements were extracted for analysis. These GIS sets of data were then processed and homogenised in terms of the projection system (WGS-1984) and uniform dimension of cells (refer to Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Criteria for Landfill Selection\u003c/h2\u003e\u003cp\u003eA landfill location must adhere to essential conditions to prevent groundwater and surface water contamination, as well as soil pollution. Additionally, considerations for settlements and infrastructure are crucial for public health. Proximity to existing roads reduces transportation costs (Thulasi et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This study employs ten criteria for evaluating landfill suitability, drawing from Indian standards, regulations, and published literature. The methodology involves separate maps for each criterion, culminating in a final composite map through Weighed Overlay Analysis (WOA). The following sections examine these landfill site selection factors in more detail, as depicted in the Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\u003cdiv id=\"Sec6\" class=\"Section3\"\u003e\u003ch2\u003e\u003cb\u003e3.2.1\u003c/b\u003e Land Elevation\u003c/h2\u003e\u003cp\u003eLand elevation significantly impacts landfill site construction and operation, as a negative correlation exists between site suitability and elevation height. To account for this, an elevation map was created using ArcGIS tools (Khan et al. 2023). The elevation of the research region is between 267 to 909 metres above mean sea level (MSL). Three distinct buffer zones were established (refer to Fig.\u0026nbsp;2a) and given weight values according to how suitable their elevation is for choosing a dump site. Areas with elevations below 300 meters were deemed highly suitable, those between 301 and 500 meters were considered moderately suitable, and regions with elevations above 501 meters were classified as least suitable and less preferable for landfill construction (see Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cdiv id=\"Sec7\" class=\"Section3\"\u003e\u003ch2\u003e\u003cb\u003e3.2.2\u003c/b\u003e Slope\u003c/h2\u003e\u003cp\u003eMorphology of land in any place is measured using slope gradient that is normally expressed as a percentage or as an angle (Thulasi et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). A landfill construction would not be technically feasible on areas with steeper slopes because this leads to leachate migration besides Soil and pollution of water (Olorunlana et al. 2022; Ali et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and unsuitable from an economic standpoint to build landfills (Premsudha et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The slope layer map was created using Arc GIS and SRTM DEM data for the study region. Terrain of the region has a ranging gradient from \u0026lt;\u0026thinsp;10\u0026deg; to \u0026gt;\u0026thinsp;20\u0026deg; that was later reclassified into steep (\u0026gt;\u0026thinsp;20\u0026deg;), moderate (10\u0026deg;- 20\u0026deg;) and plane (\u0026lt;\u0026thinsp;10\u0026deg;) areas assigned weightage from 0\u0026ndash;1 respectively (Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Areas with weightage of 1 were considered as the most suitable since they had slope\u0026thinsp;\u0026lt;\u0026thinsp;10\u0026deg; which is highly favourable for sanitary landfills (Fig.\u0026nbsp;2b) while those with slopes over 20 ̊ were deemed unsuitable for landfilling operations.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section3\"\u003e\u003ch2\u003e\u003cb\u003e3.2.3\u003c/b\u003e Lithological Structure\u003c/h2\u003e\u003cp\u003eIt is vital to consider the lithological structure while selecting a landfill location. The explanation for the lithological structure is according to Geological Survey of India (GSI), India. One way geology influences this is by controlling infiltration rate and low infiltration formation is considered desirable for garbage disposal because it prevents pollutants from leaking from the site of disposal to ground water. The ranking for this sub-criterion can be seen on Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Nine lithological formations are found in the study area (Fig.\u0026nbsp;2c). Landfill development in the study area should prioritize zones weighted 0.75 to 1, with a focus on lithology rich in clay or shale which ensures low permeability and good containment, suitable for landfill site (Majid \u0026amp; Mir \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Zones weighted 0.5 may be considered with additional engineering safeguards and zones weighted 0- 0.25 are least or unsuitable due high permeability or poor containment characteristics which can cause high environmental risks and should be avoided.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\u003ch2\u003e\u003cb\u003e3.2.4\u003c/b\u003e Soil Texture\u003c/h2\u003e\u003cp\u003eThe soil texture of study area is primarily divided into three regions as per India-WRIS, Ministry of Jal Shakti illustrated in Fig.\u0026nbsp;2d. Soil with fine particles (e.g., clay or silty clay) are ideal for landfills because they offer low permeability and act as a natural barrier, reducing the risk of leachate seepage into groundwater (Alkaradaghi et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) so are given high weightage i.e., 1. On the other hand, rocky and non-soil are least or unsuitable for landfills as they are highly permeable and lack the structural and chemical properties to retain contaminants or support landfill infrastructure effectively so are given least weightage i.e., 0.25 (as shown in Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section3\"\u003e\u003ch2\u003e\u003cb\u003e3.2.5\u003c/b\u003e Landuse and Landcover (LULC)\u003c/h2\u003e\u003cp\u003eIn this research, the Landuse and Landcover (LULC) map encompasses regions such as built-up areas, water bodies, forest cover, agricultural land, rangeland, and bare ground. High resolution data (Sentinel-2 having a 10-meter resolution) is employed for classifying land usage. From an economic standpoint, bare ground and rangeland are deemed the most suitable options for a landfill site, as they can be sold post-landfill completion and face less public resistance (Suchitra \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). In the study area, bare ground and rangeland are assigned the highest suitability weightage i.e., 1 for establishing a landfill site (Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), while water bodies and built-up regions are determined undesirable due to leachate contamination, ecological sensitivity and habitat destruction, so are given least weightage. Agricultural land are assigned as moderate suitable because landfills could affect soil fertility and probable detrimental effects on residential areas, such as odour, noise and dust (Kosoe et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The suitability is visually represented in Fig.\u0026nbsp;2e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section3\"\u003e\u003ch2\u003e\u003cb\u003e3.2.6\u003c/b\u003e Distance from Roads\u003c/h2\u003e\u003cp\u003eIn this research, the choice of feasible locations for landfills takes into consideration the closeness to roadways, because rising construction and transportation expenses arise with increased gaps among garbage generating locations and potential dumping areas (Das and Bhattacharyya \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Guler and Yomralioglu \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). While certain studies suggest locating waste disposal sites at a greater distance from road systems to address aesthetic and ecological concerns (Kalisha et al. 2024). In such cases, maintaining a certain distance from roads can reduce transportation costs. In order to tackle these elements, two ring buffers (less than 100 meters and more than 100 meters) are established around the road system (Fig.\u0026nbsp;2f). Each buffer zone was then given a weightage according to specific placement criteria (Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section3\"\u003e\u003ch2\u003e\u003cb\u003e3.2.7\u003c/b\u003e Distance from the River\u003c/h2\u003e\u003cp\u003eIn India, there is a legal restriction preventing the disposal of solid trash in or near any water surface, including rivers or lakes (CPCB, 2008). Consequently, maximum distance from any water body holds significant weight in selection of site, while minimum distance canals to rivers is deemed inappropriate for location choice (CPHEEO, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). In this study, area under the buffer zone of 500 meters is deemed least or unsuitable for landfill construction and is weighted 0.25, also the area more than 1000 meters from the river is weighted as the most suitable i.e., 1 (Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The suitability map based on proximity to rivers is demonstrated through Fig.\u0026nbsp;2g.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section3\"\u003e\u003ch2\u003e\u003cb\u003e3.2.8\u003c/b\u003e Normalized Difference Vegetation Index (NDVI)\u003c/h2\u003e\u003cp\u003eVegetation cover acts as a natural barrier, minimizing the spread of contaminants through wind (Lin et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). To analyze vegetation's influence on aerial dispersion, the NDVI (Normalized Difference Vegetation Index) was utilized, as it effectively represents vegetation's impact on contaminant movement (Duarte et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). This index enables the identification, classification, and estimation of vegetation or biomass in a given area (Hamimina et al., 2013). In this research, NDVI values were derived using satellite imagery from Resourcesat 2A LISS IV. A threshold value of 0.5 is applied for feature delineation. The NDVI map ranges from less than 0.5 to more than 0.5, where values greater than 0.5 signify healthy vegetation, values near 0 indicate a lack of vegetation, and values approaching lower than 0.5 represent water bodies. On the NDVI map, vegetated regions are visually distinguished by a green colour (Fig.\u0026nbsp;2h).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section3\"\u003e\u003ch2\u003e\u003cb\u003e3.2.9\u003c/b\u003e Distance from Sensitive and Restricted Places\u003c/h2\u003e\u003cp\u003eAccording to the Indian Municipal Solid Waste Management Rules of 2016, sanitary landfill sites must not be located near sensitive areas such as children's parks, natural parks, offices, banks, or critical habitats and eco-fragile zones (Yadav et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). As an expanding urban area, it is crucial to account for this guideline, ensuring that landfill sites are not established close to restricted or sensitive locations (Kontos et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Guler and Yomralioglu \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). This study recommends that areas within 100 meters of sensitive and restricted zones, including schools, colleges, banks, railway stations, children's parks, and offices, should be deemed unsuitable for landfill sites (Ali et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Instead, locations at a greater distance should be selected for such purposes (Fig.\u0026nbsp;2i).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section3\"\u003e\u003ch2\u003e\u003cb\u003e3.2.10\u003c/b\u003e Aquifers\u003c/h2\u003e\u003cp\u003eAquifers play a vital role in assessing the suitability of landfill sites as they are integral to the groundwater system. Landfills produce leachate, a potentially hazardous liquid generated when water filters through waste, which can contaminate aquifers without adequate safeguards (Vasoogh et al. 2017). In the study area, three primary aquifers rocks are identified: limestone/dolomite, older alluvium (comprising silt, sand, gravel, and lithomargic clay), and pebble/gravel/bazada/kandi. Aquifers with high permeability, such as gravel or karstic limestone/ dolomite, facilitate rapid leachate migration, posing a significant risk of contamination (Ivana et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and therefore receive lower weightage i.e., 0. Conversely, older alluvium aquifers with low permeability are more effective in containing leachate, making them more favourable, hence given high weightage i.e., 1 (refer Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Implementing protective measures, such as liners and leachate collection systems, is crucial to ensure environmentally sustainable landfill sites (Fig.\u0026nbsp;2j).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eOverview of Rankings and Suitability Levels Used in Choosing a Potential Landfill Site in the Study Area\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCriteria\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSub- criteria/alternatives\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSuitability index (Weightage)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLevel of Suitability\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eTotal Area (in %)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eReferences\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eDistance to roads (m)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHigh Suitable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e92.762\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eAli et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e),\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLow suitable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e7.237\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eZondi et al. (2023)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eDistance to river (m)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;1000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHigh suitable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e58.617\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eKosoe et al. (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e500\u0026ndash;1000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eModerate Suitable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e15.532\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eElmo et al. (2023)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eUnsuitable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e25.850\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ePasalari et al. (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2019\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"8\" rowspan=\"9\"\u003e\u003cp\u003eLithological Structure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eShale with Lenticles of Limestone\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eVery High suitable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.173\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ePremsudha et al. (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGrey Sand, Silt and Clay\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eVery High suitable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e11.772\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eŞener et\u0026nbsp;al. (2011)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSilt, Clay, Sand with Gravel and Pebbles\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHigh suitable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e54.496\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGrey Micaceous Sand, Silt and Clay\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHigh suitable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e24.018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eArgillaceous Limestone and Clay\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eModerate suitable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.429\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLimestone, Dolomitic Limestone with Shale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eModerate suitable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.283\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDiamictite, Quartzite, Slate and Boulder Bed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eModerate suitable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e6.255\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCarbonaceous Shale, Slate, Greywacke\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLow suitable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.403\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMassive Sandy Limestone\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eUnsuitable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.165\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eSlope ( ̊ )\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHigh suitable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e83.048\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eOlorunlana et al. (2022),\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10\u0026ndash;20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eModerate suitable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e9.217\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eUnsuitable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e7.733\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eAli and Ahmad (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e),\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eElevation (m)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e267\u0026ndash;300\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHigh suitable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e28.736\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eKhan et al. (2023);\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e301\u0026ndash;500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eModerate suitable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e65.781\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eŞener et\u0026nbsp;al. (2010, 2011),\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e501\u0026ndash;909\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLow Suitable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5.482\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eSoil Texture\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFine Texture\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHigh Suitable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e32.693\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eAli et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e); Adar et al. (2023)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMedium Texture\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eModerate Suitable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e24.367\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eKapilan and Elangovan (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2018\u003c/span\u003e); Paul et al. (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2014\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRocky and Non- Soil\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLow Suitable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e42.938\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(Alkaradaghi et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2019\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e\u003cp\u003eLanduse/Landcover (LULC)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBareground\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHigh suitable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e9.519\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eUpadana et al. (\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2023\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRangeland\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHigh Suitable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.231\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eThulasi et al. (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAgricultural Land\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eModerate suitable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.732\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eAli and Ahmad (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eForest Cover\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLow Suitable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e46.341\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWater Bodies\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eUnsuitable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4.943\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBuilt-up Areas\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eUnsuitable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e35.231\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNDVI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHigh suitable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e81.767\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eLin et al., (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLow Suitable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e18.232\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eDuarte et al., (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2014\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDistance to Sensitive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;301\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHigh suitable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e94.558\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eYadav et al., (\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlaces (m)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e201\u0026ndash;300\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eModerate Suitable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.815\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eKontos et al., (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2005\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;200\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eUnsuitable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.625\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eGuler and Yomralioglu (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2017\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAquifers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOlder Alluvium (silt, sand, gravel, and lithomargic clay)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHigh suitable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e58.413\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eVasoogh et al. 2017\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePebble/Gravel/Bazada/Kandi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eModerate Suitable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e18.275\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eIvana et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLimestone/Dolomite\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eUnsuitable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e23.310\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eSource: Computed by the author(s)\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Analytic Hierarchy Process (AHP): A Multicriteria Technique\u003c/h2\u003e\u003cp\u003eAHP involves organizing the chosen criteria into a system of hierarchy based on the overarching objective (Kosoe et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In practice, this technique consists of numerous stages, including building a decision structure for the criteria, calculating their relative relevance, analysing priorities for each criterion, deriving ultimate objectives, and assessing the decision's sensitivity (Paul et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Saaty, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e1980\u003c/span\u003e). However, in practice, AHP's primary limitation is the use of a precise numerical value, which may be insufficient due to subjectivity in human perspectives and rating for comparison matrices (Ali and Ahmad, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Despite this, AHP effectively addresses complex decision-making scenarios in real-life situations and often provides better outcomes compared to other MCDM techniques, particularly when integrated with geographical information systems and spatial data with geographic information system and spatial data (Ali and Ahmad \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2019a\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003eb\u003c/span\u003e). Within the scope of this research, the Analytic Hierarchy Process was utilized as a process to develop decisions based on multiple criteria, working alongside Geographic Information Systems (GIS) were used to efficiently select the best dump location for the Rishikesh Municipal Corporation.\u003c/p\u003e\u003cp\u003eAll criteria and sub criteria were given weightage and ranking according to their level of suitability as illustrated in Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e4\u003c/span\u003e presenting the pair wise correlation between these parameters. In order to determine the significance of the selected criteria, the research utilized numerical ratings derived from Saaty's Pair-wise Comparison Method, which spans between 1 and 9.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec17\" class=\"Section3\"\u003e\u003ch2\u003e\u003cb\u003e3.3.1\u003c/b\u003e Calculating weight normalisation:\u003c/h2\u003e\u003cp\u003eTo generate a normalized pair-wise comparison matrix, the following equation is used:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:NW=GM\\frac{GM}{\\:{Ʃ}^{N\\:}n-1}{GM}_{n}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eWhere, NW refers to the calculation of Normalized weights, while GMn stands for the geometric mean computation of the nth row in Px.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCriteria Weight of Ten Thematic Aspects for Efficient Landfill Site Determination\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCriteria\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCriteria Weights\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRank\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDRV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eST\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLULC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNDVI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDSP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAQ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9\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\u003cb\u003e(DR\u003c/b\u003e distance to roads, \u003cb\u003eDRV\u003c/b\u003e distance to river, \u003cb\u003eLS\u003c/b\u003e lithological structure, \u003cb\u003eSL\u003c/b\u003e slope, \u003cb\u003eLE\u003c/b\u003e land elevation, \u003cb\u003eST\u003c/b\u003e soil texture, \u003cb\u003eLULC\u003c/b\u003e land-use and land-cover, \u003cb\u003eNDVI\u003c/b\u003e normalized difference vegetation index, \u003cb\u003eDSP\u003c/b\u003e \u003cem\u003edistance to\u003c/em\u003e sensitive places, \u003cb\u003eAQ\u003c/b\u003e aquifers)\u003c/p\u003e\u003cp\u003e\u003cem\u003eSource: Computed by author(s)\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section3\"\u003e\u003ch2\u003e\u003cb\u003e3.3.2\u003c/b\u003e Consistency Index (CI)\u003c/h2\u003e\u003cp\u003eAfter calculating weights and providing ranking to each parameter (Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) we have to calculate consistency index. The Consistency Index formula in Saaty's analytic hierarchy process approach is explained as:\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:CI=\\frac{({\\lambda\\:}_{max}-n)}{n-1}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe term 'λ max' refers to the eigenvalue of the comparison matrix. The given equation explains that the product of Px with the weight of each criterion creates the consistency vector, which in this case is 9.564. N represents the total number of elements involved. The Consistency Index (0.070), as calculated, is presented in Table \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. To determine Consistency Ratio (CR), it is necessary to use this CI value.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section3\"\u003e\u003ch2\u003e\u003cb\u003e3.3.3\u003c/b\u003e Consistency ratio (CR)\u003c/h2\u003e\u003cp\u003eFor ensuring the significance of the derived weight, it is crucial to assess the consistency of the results. For this, we need to compute the value of Consistency Ratio (CR) of the Paired Comparison Matrix (PCM). If the level of consistent ratio is greater than 0.1 or 10%, the decision-making process is deemed inconsistent and must be repeated. Conversely, a CR of 0 implies a perfectly consistent decision. The formula of CR:\u003c/p\u003e\u003cp\u003e\u003cb\u003eCR\u003c/b\u003e\u003cb\u003e=\u003c/b\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{\\varvec{C}\\varvec{I}}{\\varvec{R}\\varvec{I}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003cp\u003eIn this formula, The Consistency Ratio is denoted as CR, the Consistency Index as CI, and the Random Index as RI. To calculate the Random Index (RI), we used Saaty's index table.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe outcome of the consistency check for all thematic aspects combined in the landfill suitability process\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eλ\u003csub\u003emax\u003c/sub\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eConsistency\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e9.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eCR\u0026thinsp;\u0026lt;\u0026thinsp;0.1(Yes)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cem\u003eSource: Computed by the author(s)\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe RI value for the ten parameters remains constant at 1.49. The assessment, having a meticulously calculated Consistency Ratio (CR) of 0.05, is considered entirely consistent. Consequently, AHP pair-wise matrix's judgment is deemed valid for selecting suitable waste management sites within the Rishikesh Municipal Corporation.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"4 Results and Discussion","content":"\u003cp\u003eIn this research, a set of ten parameters, encompassing environmental and economic aspects relevant to the study area's issues, were established. These criteria were mapped using GIS and assigned weights according to the AHP methodology. Evaluation of these criteria adhered to the MSWM Rules, 2016, India, and relevant literature. A matrix for comparing pairs was created to evaluate the significance of each thematic layer, leading to an acceptable consistency ratio (CR at 0.05). Suitability weights for each parameter, indicate that the parameter of land use and land cover, distance from river, sensitive places and normalized difference vegetation index have been assigned to the highest values at 0.13 and 0.11, respectively, while Lithological Structure, aquifers and slope have the lowest values at 0.07, 0.08 and 0.09, respectively, among the ten essential criteria, as shown in Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Importance given to the parameter of land use and land cover in Rishikesh stems from its status as a rapidly developing city in Uttarakhand, known for its popularity as a tourist destination and the global Yoga capital. As a result, selecting a site on the city's outskirts with a lower population density and settlement than the central area is recommended. Furthermore, the distance from the Ganga River is highlighted because landfill sites can lead to water and soil pollution, potentially harming human health. The pollution of the Ganga River along its course through the plains, caused by nearby dump sites, makes these locations unsuitable. The lowest weightage is assigned to lithological structure, aquifers, and slope because the areas deemed unsuitable are located along the river or predominantly on its eastern and north eastern sides, which have already been classified as unsuitable due to their proximity to the river.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eLevel of Suitability, Suitable Areas, and the Percentage of Total Area Coverage\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSuitability Levels\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWeightage\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eArea (sq.km)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTotal Area Coverage (in %)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eVery High Suitable\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.503\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.066\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHigh Suitable\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.683\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.773\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eModerate Suitable\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e20.514\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e29.666\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLow Suitable\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e23.013\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e33.279\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eUnsuitable\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e17.435\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e25.213\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cem\u003eSource: Computed by the author(s)\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eLand-use and land-cover have been given higher emphasis in the suitability assessment, as it should avoid placing landfills near populated zones, and is crucial to keep them distant from urban developments, thriving greenery, water sources, and fertile farming regions, particularly in the area under investigation. Among these, bare ground and rangeland have been identified as the most suitable locations for landfills. Distance from roads and slope are also crucial factors in determining weight scores. The GIS environment merges the criteria and sub-criteria weights into thematic layers, generating a map of landfill suitability. The map is categorized into five groups: Very High Suitable, High Suitable, Moderate Suitable, Low Suitable and Unsuitable as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e5\u003c/span\u003e. According to the map, it is indicated that 6.7% of the Rishikesh Municipal Corporation region is appropriate, 29.6% can be considered moderately suitable, 33.2% has lesser suitability, 25.2% is deemed unsuitable and only 5% area is highly appropriate for making a suitable landfill site region (refer to Table \u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Ideal landfill locations should be in areas characterized by low elevation, minimal slope, considerable distance from roads and rivers, and a limited presence of residences. These factors contribute to the overall suitability of the site for landfill purposes.\u003c/p\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003e4.1 Model Evaluation and Validation\u003c/h2\u003e\u003cp\u003eThe predictive performance of the final suitability map generated using AHP was evaluated using the receiver operating characteristic (ROC) value and the area under the ROC curve (AUC). The ROC curve is a graphical representation that illustrates binary classification performance by varying a threshold. It plots the true positive rate (TPR), or sensitivity, against the false positive rate (FPR), which is calculated as 1-specificity (Nandi et al., 2010). An ideal ROC curve approaches the top-left corner of the plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e6\u003c/span\u003e). To better assess the ROC curve and model accuracy, the AUC score is calculated, ranging from 0.5 (random guessing) to 1 (perfect fit) (Fawcett, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). In this study, since the region is considered small, the number of potential sites is relatively low, the AUC-ROC curve was used with a validation dataset of 8 GPS points obtained through Google Earth imagery and area visits to assess the accuracy of AHP in landfill site suitability mapping. The landfill suitability map was quantitatively validated by computing the AUC, resulting in a value of 0.855 or 85.5%, which lies within the good range of 0.8 to 1, indicating highly reliable results (Kamdar et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eHigh-resolution Google Earth images and study field survey further confirmed that the identified suitable areas primarily consisted of open or bare land, demonstrating the model's strong predictive capability in pinpointing these locations (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e7\u003c/span\u003e). Six primary candidate locations were identified, with the fourth site aligning with the government-proposed location for a scientific landfill at Lalpani Beet No-1, Rishikesh. This site is designated for waste treatment and includes facilities for RDF (Refuse-Derived Fuel), composting, and secured landfill. The plant will have a capacity of 240 TPD (tons per day) and will also feature a leachate treatment plant. This alignment validates the site selection process, demonstrating that the ROC-AUC method is highly effective for the study region.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"5 Conclusion and Suggestion","content":"\u003cp\u003eThe rapid population growth and evolving lifestyle in Rishikesh have resulted in a substantial rise in Municipal Solid Waste (MSW), causing challenges in efficient management by the local authorities due to rising land costs, limited budgets, and traditional waste disposal practices. In the presented research, a site suitability model that incorporates Geographic Information Systems (GIS) can aid professionals such as researchers, urban designers, civil engineers, decision-makers and officials are involved in determining the most acceptable places for building waste disposal facilities. This approach aims to maintain waste management sustainability and safeguard public health from potential hazards like atmospheric impurity, water contamination, unpleasant scents, and hazardous gas releases from waste combustion. As a tourist destination, Rishikesh already faces public health issues due to traffic-related CO2, NO2, and SO2 emissions, as well as increased waste generation from the floating population. This research presents a swift, evidence-backed approach for making decisions on MSW disposal, enhancing public health and fostering ecological sustainability.\u003c/p\u003e\u003cp\u003eThe study collected and systematized pertinent information related to particular criteria within a Geographic Information System (GIS) setting. The Weighted Overlay Analysis (WOA), a widely accepted method, was employed to create a suitability map based on the collected information. The Analytical Hierarchy Process (AHP) was employed to allocate importance and execute an overlay analysis for the most suitable location choice. This approach not only identified areas with potential environmental risks but also excluded them as unsuitable options for site selection.\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eIn cities like Rishikesh, which attract pilgrims, there is a notable disparity between the generated waste and waste disposal. Among the 40 wards in the city, 20 wards have a predominantly rural setting, where the input of wet waste into the collection system is comparatively low. This is due to the practice of feeding organic residues to animals or repurposing waste flowers for incense production. Consequently, a substantial portion of organic waste remains excluded from the urban waste collection system.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eIn the Landfill Site Suitability Map, a significant portion of the city center is deemed unsuitable or least suitable for waste disposal facilities. This is attributed to factors such as densely populated area, allocation of an urban center, expensive prices of land, the proximity to a continuously flowing river, and the influence of geomorphic and lithological characteristics.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eThe 500-meter-wide stretch alongside the Ganga River is entirely unsuitable for landfills, despite having a considerably higher waste generation rate compared to peripheral areas. This is due to the existing waste disposal site in Govind Nagar ward, which falls within a 500-meter zone from the riverbank.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eRishikesh currently lacks adequate scientific facilities for processing, sorting, or treating dry waste. Consequently, both dry and majority of wet waste from residential, commercial, institutional, hotel, and other waste generation sources are collected and transported to the dumping ground situated in Govind Nagar.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eThe waste disposal site, spanning around 6.5 hectares, is an open area without any scientific waste management provisions. Due to its unplanned layout, it lacks a designated capacity limit. Despite the site's central location within the city and proximity to residential areas, expanding it is not viable. Consequently, waste accumulation has led to an increase in the dumping ground's height. This fluctuating elevation makes it difficult to accurately measure the volume of waste disposed of.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eIn terms of environmental considerations, the decision has been made to avoid choosing landfill sites near surface water sources. Consequently, the primary factors taken into account when choosing options were economic and environmental considerations. The optimal spots for managing waste disposal predominantly lie within the south and south-western regions of the research zone as demonstrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e5\u003c/span\u003e, covering approximately 5% of the region.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eIn the post-processing evaluation, the appropriate location was re-examined, and ground verification was conducted to ascertain if all ecological and social criteria were met. Additionally, the final acceptable and unacceptable zones were reviewed with the municipal department, confirming the selected sites. They also mentioned that a new landfill site proposal had been submitted to the Ministry of Environment, situated within the suitability zone of the map results, specifically in the south-western region. Consequently, the findings support the new proposed landfill site for Rishikesh Municipal Corporation, highlighting its relevance and consistency.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eIn summary, this research suggests implementing its findings to address landfill site selection challenges in Rishikesh city and other rapidly urbanizing areas worldwide that confront similar issues.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConflict of interest:\u003c/strong\u003e The authors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets analyzed during this study are derived from publicly accessible sources, Geological data is available at the Bhukosh Portal () by Geological Survey of India (GSI), Soil texture and Aquifer type data is available at Water Resource Information System (WRIS) (), ALOS DEM has been downloaded from NASA Earth Data (), LULC from Esri Inc. (), and the high resolution Resourcesat-2A LISS IV Satellite images from Bhoonidhi (). The study area shape file have been obtained from Rishikesh Municipal Corporation office. GCPs have been taken using Garmin GPS.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbdul-Wahab, S. A. (2020). Modelling methane and vinyl chloride in soil surrounding landfill. International Journal of Environmental Pollution, 21, 339\u0026ndash;349 https://doi.org/10.1007/s42489-020-00052-1\u003c/li\u003e\n\u003cli\u003eAdar, E., Adar, T. (2023). Comprehensive Evaluation of Hazardous Solid Waste Treatment and Disposal Technologies by a New Integrated AHP\u0026amp;MARCOS Approach. International Journal of Information Technology \u0026amp; Decision Making. http://dx.doi.org/10.1142/S0219622023500372\u003c/li\u003e\n\u003cli\u003eAgamuthu, P., Haji Salleh, S., Khidzir, K. M., \u0026amp; Noorazamiah, A. (2019). Sustainable waste management: Asian perspectives. In International Conference on Sustainable Solid Waste Management (pp. 15-26), Chennai, India.\u003c/li\u003e\n\u003cli\u003eAksoy, E., \u0026amp; San, B. T. (2019). Geographical information systems (GIS) and multi-criteria decision analysis (MCDA) integration for sustainable landfill site selection considering dynamic data source. Bulletin of Engineering Geology and the Environment, 78(2), 779\u0026ndash;791 DOI: \u003cu\u003e10.1007/s10064-017-1135-z\u003c/u\u003e\u003c/li\u003e\n\u003cli\u003eAli, S. A., \u0026amp; Ahmad, A. (2019a). Mapping of mosquito-borne diseases in Kolkata Municipal Corporation using GIS and AHP based decision making approach. Spatial Information Research, 1\u0026ndash;16 DOI: \u003cu\u003e10.1007/s41324-019-00242-8\u003c/u\u003e\u003c/li\u003e\n\u003cli\u003eAli, S. A., \u0026amp; Ahmad, A. (2019b). Spatial susceptibility analysis of vector-borne diseases in KMC using geospatial technique and MCDM approach. 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S. (2024). Selecting the Suitable Landfill Location for Efficient Waste Management in Udham Singh Nagar District, Uttarakhand: A Geospatial and Multi-Criteria Approach. Papers in Applied Geography. https://doi.org/10.1080/23754931.2024.2403682\u003c/li\u003e\n\u003cli\u003eVosoogh, A., Baghvand, A., Karbassi, A. (2017)\u003cem\u003e.\u003c/em\u003e Landfill Site Selection Using Pollution Potential Zoning of Aquifers by Modified DRASTIC Method: Case Study in Northeast Iran. The \u003cem\u003eIranian Journal\u003c/em\u003e of \u003cem\u003eScience\u003c/em\u003e and \u003cem\u003eTechnology\u003c/em\u003e, Transactions of \u003cem\u003eCivil Engineering\u003c/em\u003e 41, 229\u0026ndash;239 (2017). https://doi.org/10.1007/s40996-017-0054-3\u003c/li\u003e\n\u003cli\u003eZondi, N., \u0026amp; Qwatekana,Z. (2023). Modernisation of Rural Communities: Solid Waste Management Implication. African Journal of Inter/Multidisciplinary Studies, 5(S1): 1-11. https://doi.org/10.51415/ajims.v5i1.114997\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Hemwati Nandan Bahuguna Garhwal University","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":"Analytic Hierarchy Process, Geographic Information System, Landfill Sites, Municipal Solid Waste, Multi Criteria Decision Analysis, Himalayan Mountains","lastPublishedDoi":"10.21203/rs.3.rs-7321392/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7321392/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAddressing the daily generation of approximately 80 metric tons of waste in Rishikesh, the Municipal Corporation faces significant challenges in solid waste management. Identifying adequate landfill sites is critical for authorities, and the purpose of this study is to identify the best areas for municipal solid waste disposal in Rishikesh. Geographic Information Systems (GIS) and Analytic Hierarchy Process (AHP) have been used as effective methods for Multiple Criteria Decision Analysis (MCDA) in waste management. Ten parameters, including distance from rivers, distance from road networks, lithological structure, elevation, soil texture, slope, and land use and land cover (LULC), normalized difference vegetation index (NDVI), distance to sensitive and restricted places and aquifers were measured for site analysis in all 40 wards of Rishikesh. The predictive maps were assessed using the receiver operating characteristic (ROC) method. A random selection of 8 potential landfill site locations was used, yielding an accuracy of 85.5% (AUC\u0026thinsp;=\u0026thinsp;0.855) for the AHP model, demonstrating high reliability. The research site has been separated into various zones, designated as areas of very high, high, moderate, low, and extremely low appropriateness, accounting for 5%, 6.7%, 29.6%, 33.2%, and 25.2% of the total area, respectively. These zones were suitable for landfill purposes. By optimizing distances from the considered parameters, the study identified potential waste management sites. This information can assist urban planners and authorities in implementing successful urban waste management strategies.\u003c/p\u003e","manuscriptTitle":"Optimizing Sustainable Landfill Sites in Rishikesh: Integrating Geospatial and MCDA for Waste Management in Himalayan Foothills","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-08 10:24:03","doi":"10.21203/rs.3.rs-7321392/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":"6f193e8f-af3a-4ba4-b83c-ecfdcde498d9","owner":[],"postedDate":"August 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":52832591,"name":"Physical Geography"},{"id":52832592,"name":"Geographic Information Systems"},{"id":52832593,"name":"Environmental Policy"}],"tags":[],"updatedAt":"2025-08-08T10:24:03+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-08 10:24:03","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7321392","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7321392","identity":"rs-7321392","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}