Geospatial Modelling and Multi-Criteria Evaluation of Groundwater Recharge Potential in a Climate-Stressed Coastal Basin of Western India

Geospatial Modelling and Multi-Criteria Evaluation of Groundwater Recharge Potential in a Climate-Stressed Coastal Basin of Western India | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Geospatial Modelling and Multi-Criteria Evaluation of Groundwater Recharge Potential in a Climate-Stressed Coastal Basin of Western India Tejas Naik, Satyajit Gaikwad, Sneha Sawant, Praveen Kamble This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9140855/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 15 You are reading this latest preprint version Abstract Groundwater depletion in coastal regions has intensified due to increasing anthropogenic pressures, particularly from ecotourism and climate-induced variability. The Mochemad River Basin, situated along the western coast of Maharashtra, India, is experiencing critical groundwater stress, including severe seawater intrusion. Despite receiving over 3500 mm of annual rainfall, inadequate recharge infrastructure has limited the sustainable replenishment of aquifers. This study employs a geospatial modelling approach integrated with the Analytical Hierarchical Process (AHP) to delineate Artificial Groundwater Recharge Zones (AGRZ) in this climate-vulnerable coastal basin. Seven thematic layers—lithology, geomorphology, land use/land cover, slope, drainage density, lineament density, and rainfall—were processed and weighted using AHP to reflect their relative influence on recharge potential. A weighted overlay analysis within a GIS environment produced a recharge suitability map categorizing the basin into four zones: unsuitable (14.24%), moderately suitable (19.25%), highly suitable (55.94%), and very highly suitable (10.56%). Model validation using the Receiver Operating Characteristics (ROC) curve yielded an Area Under the Curve (AUC) of 0.89, indicating strong predictive performance. The study’s novelty lies in the integration of high-resolution spatial datasets with AHP in a high-rainfall, climate-sensitive coastal context. Based on the identified zones, suitable site-specific recharge interventions such as check dams, infiltration tanks, Mati Bandaras, and Continuous Contour Trenches (CCTs) are recommended to enhance groundwater resilience and sustainable water resource management in the region. Groundwater Recharge Modelling Analytical Hierarchical Process (AHP) Geospatial Techniques Coastal Aquifer Management Climate Vulnerability Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Introduction Groundwater is an indispensable freshwater resource supporting domestic, agricultural, and industrial activities across the globe. It constitutes nearly 95% of the planet’s available freshwater (Oksana and Dmytro 2021 ), and its sustainable management is crucial, especially in regions facing growing water stress due to climatic variability, population growth, and urban expansion (Kumari and Singh, 2021 ; Yazdi et al., 2024 ). Coastal regions face unique challenges such as over-extraction of groundwater and subsequent seawater intrusion, which compromises freshwater availability and quality (Bhattacharya, 2010; Rajasekhar et al., 2020 ; Howlader et al., 2024 ). This concern is evident in the southern Konkan region, including the Mochemad River Basin, which has shown declining groundwater levels over decadal timescales (Das, 2022 ; Gaikwad et al., 2020 ). Artificial groundwater recharge (AGR) is recognized as a sustainable and effective solution to mitigate groundwater depletion and seawater intrusion in coastal aquifers (Pitchaimani et al., 2024 ; Hasan et al., 2022 ). The integration of geospatial technologies with decision-support models such as the Analytical Hierarchical Process (AHP) has proven successful for mapping groundwater recharge potential zones (Saaty, 1980; Patel et al., 2022; Zghibi et al., 2020 ). AHP enables systematic evaluation of multiple spatial factors—including geology, slope, drainage density, lineament density, and land use—by assigning relative weights to reflect their influence on recharge (Saaty, 1988; Kaliraj et al., 2014 ). Recent studies by Ali et al. ( 2024 a) have demonstrated the effectiveness of machine learning ensemble models for delineating groundwater potential zones in varied hydrogeological settings like the Lidder watershed, India, providing a valuable comparison for evaluating model accuracy and geospatial integration techniques. Similarly, Ali et al. ( 2024 b) have emphasized watershed prioritization using morphometric analysis, reinforcing the importance of topographic and drainage parameters in recharge modelling—a key aspect also addressed in this study. In addition, Ali et al. ( 2025 ) assessed climate change trends using non-parametric methods and machine learning, underscoring the need to integrate such insights when dealing with dynamic recharge conditions in climate-sensitive coastal regions. These studies have demonstrated the effectiveness of hybrid approaches combining AHP and remote sensing/GIS in a variety of geohydrological contexts. For instance, Khan et al. ( 2023 ) applied AHP and GIS in Saudi Arabia’s arid environments to delineate aquifer recharge zones with high predictive accuracy. Similarly, Saha et al. ( 2023 ) utilized AHP for the Deccan basaltic region of India, reinforcing the utility of this method in complex volcanic terrains. Sathiyamoorthy et al. ( 2023 ) highlighted how AHP-GIS integration in coastal Tamil Nadu can inform sustainable recharge strategies by accounting for regional geomorphology and land use. These studies provide essential frameworks that validate the method's applicability across diverse hydrogeological settings. Despite these advancements, limited studies have focused specifically on high-rainfall coastal environments like the Mochemad River Basin, which experiences over 3500 mm of annual precipitation yet suffers from saline intrusion and groundwater scarcity post-monsoon. Moreover, no prior attempt has been made to model artificial recharge zones in this basin using AHP-GIS techniques. The present study addresses this gap by developing a spatially explicit recharge suitability model based on high-resolution geospatial datasets and validated through ROC–AUC analysis. The novelty of this research lies in its application of a multi-criteria AHP-based approach tailored to the hydrogeological complexity of a coastal river basin in the Western Ghats foothills. The study identifies site-specific recharge structures such as check dams, percolation tanks, and continuous contour trenches, providing practical guidance for sustainable groundwater management. This framework can be adapted for similar coastal and lateritic terrains vulnerable to saline intrusion and seasonal water shortages. Study Area The present study focuses on the Mochemad River Basin located in the Sindhudurg district, part of the southern Konkan region along the west coast of India. The basin spans across Kudal, Sawantwadi, and Vengurla tehsils and is geographically bounded by longitudes 73°39′ to 73°49′ E and latitudes 15°47′ N to 15°57′ N. It is represented on the Survey of India Toposheet Nos. 48 E/9 and 48 E/13 (scale 1:50,000). The Mochemad River originates at Humras village (elevation 131 m) in Kudal and flows southwest, draining into the Arabian Sea near Tak village. The river has a total length of 28 km and a basin area of approximately 130 km². This coastal basin experiences high annual rainfall, ranging between 3000 mm and 4700 mm (Bandaru et al., 2016 ), with an average of 3070 mm (CGWB, 2014). Despite this, the region frequently faces post-monsoon water scarcity due to rapid surface runoff, limited aquifer storage, and saline water intrusion. The basin's physiography is marked by flat-topped hills and lateritic plateaus in the east and coastal plains in the west (CGWB, 2014), which greatly influence groundwater behaviour. These hydrogeological conditions underscore the necessity of modelling and mapping artificial groundwater recharge zones to manage and conserve groundwater resources effectively. The main aquifer formations are laterites, granites, and granitic gneisses, with groundwater occurring in unconfined aquifers at shallow depths (2–10 m bgl). Dug and bore wells in coastal alluvium typically yield 2–5 m³/day, while borewells in the gneissic complex are deeper (50–70 m) and yield 500–7770 LPH. Laterite aquifers, prevalent in the northeast and south of the basin, exhibit specific capacities between 79.10 and 424.57 LPM/m drawdown, transmissivities of 46.59–375.22 m²/day, and permeabilities ranging from 7.40 to 425.22 m/day (CGWB, 2014). Given the complex hydrogeological and physiographic settings, coupled with seasonal variability, spatial modeling using GIS and AHP offers a powerful approach to delineate suitable sites for artificial recharge. This study aims to develop such a spatially explicit model tailored to the unique terrain of the Mochemad River Basin. Method and Material The multi-parametric analysis for demarcating artificial groundwater recharge areas of Mochemad watershed has been done using AHP technique in GIS environment. The current study is implemented in the following methods (Fig.2). In the study of the Mochemad River Basin, seven criterions such as Geology (GG), Geomorphology (GM), Drainage Density (DD), Slope (S), Lineament density (LD), Soil type and LULC have been analysed by AHP approach using normalized weight to demarcate area for artificial groundwater recharge. Seven spatial criterions like Geology (GG), Geomorphology (GM), Drainage Density (DD), Slope (S), Lineament density (LD), Soil type and LULC have been used for the preparation of geospatial database. Lithological and geomorphological map was prepared using district resource map of Sindhudurg district. The Survey of India (SOI) toposheet, from which the drainage density layer was created, was used to digitise the stream network. To create a slope map, SRTM DEM was taken from US Geological Survey Earth Explorer and processed. The "Manual for Geomorphology and Lineament mapping (web version)" was used to digitise the lineament map, which was then processed to determine its density. LISS 3 satellite imagery was used to create the Land Use and Land Cover (LULC) map, and the National Bureau of Soil Survey and Land Use Planning (NBSS and LUP, Nagpur) map was used to create the soil type map layer (Reshmidevi et al., 2008). Table.1: Details of the data used for the study their source Data Type Sources Data used for Topographical maps No. 48 E/9 and 48 E/13 (Scale 1:50,000) Survey of India (SOI) Drainage map GEDM (Resolution= 30m) (Scale 1:50,000) Glovis Relief, slope, drainage, topographic Geological map (Scale 1:50,000) GSI District Resource Map, Geology Geomorphological map (Scale 1:2,50,000) BHUVAN Geomorphology LULC Data (Scale 1:50,000) LISS- III satellite imagery LULC Map Soil data (Scale 1:2,50,000) National Bureau of Soil Survey and land Use Planning Soil type Lineament Data (Scale 1:50,000) Toposheet, GSI map, and GEDM (as supplementary) Lineament density and map. Groundwater fluctuation data Field data Groundwater level Multi- criteria Decision making using Analytical Hierarchical process (AHP) In this study of Mochemad River Basin, AHP is utilized to demarcate the regions for artificial groundwater recharge. The method was suggested by Saaty (1980) for solving complicated decision-making issues. Field study and experts view was used to assign the weight of the Saaty’s 1-9 scale for each thematic layer. The response of these influencing parameters is weighted as per their reaction to recharge of groundwater. A factor with high rank is the layer with high impact and factor with low rank are with low impact on the groundwater recharge. AHP was used for assigning the weights and calculate the normalized weights for the parameters influencing groundwater recharge. The Pairwise Comparison Matrix of thematic layers such as Geology, Geomorphology, Slope, Drainage Density, Soil Type, Lineament Density and LULC were compared according to the 1-9 scale suggested by Saaty (2008). The consistency ratio (CR) and consistency index (CI) values were computed to examine the reliability of the obtained results. The formula used is Where CR is consistency ratio, RI is random consistency index whose values are derived from the order of matrix. CI is consistency index which is calculated from the formula given below Where λ is the principal Eigen value of the matrix and n is the number of parameters affecting groundwater recharge. The CR should be less than 0.1 to avoid the inconsistency. Groundwater Level Data and Model Validation Approach The output map validation is done by using post-monsoon groundwater level data. The rationale behind selecting post-monsoon data lies in its effectiveness in representing the immediate recharge response of aquifers following the monsoon rainfall. During this period, water levels are typically at their peak, and the recharge from both natural infiltration and potential artificial recharge structures is most evident. This makes post-monsoon data a more reliable indicator for evaluating the effectiveness of recharge-prone zones delineated in the model. In contrast, pre-monsoon data often reflect seasonal depletion influenced by prolonged extraction and evapotranspiration, offering limited insight into the recharge potential or capacity of the terrain. Therefore, the post-monsoon period provides a more consistent and representative baseline for validating the spatial accuracy and functionality of the artificial groundwater recharge zone (AGRZ) model. This approach is consistent with other hydrological studies that assess recharge zones based on water level rise post-monsoon (e.g., Rajaveni et al., 2017; Choudhury et al., 2023). Result and Discussion Geology: The porosity and permeability of rocks determine the presence and flow of groundwater (Balaji et al. 2019; Luo et al, 2020; Khan et al, 2022). There is a variety of lithology in the research region (Deendar, 2003). Eleven distinct geological units, ranging in age from Pre-Cambrian to modern, make up the river basin under study (Gaikwad et al., 2020). A suite of Tonalite, Trondjhemite, and Granodiorite (TTG) gneisses are mostly exposed, together with granitoids and migmatites that contain enclaves of Banded Iron Formation (BIF), Amphibolite, and Ultramafite. Dolerite dykes and quartz veins penetrate the biotite TTG, which is the basement for the supracrustal rocks in the region, such as Metapellite and BIF. Laterite is developed over the country rock at higher elevations and fluvio- marine sediments are present near the mouth of the river (GSI, 2012). The homogeneously foliated Biotite-TTG gneiss and fluvio-marine deposits demonstrates high infiltration capacity and hence given highest rank (Abijith et al. 2020; Mahato et al, 2022). Both primary and secondary laterite are found in large quantities in low-lying platens and along the Western coast. They are porous, although their impermeability will limit infiltration. As a result, it has the lowest ranking. Geomorphology: Geomorphology is the most significant characteristic for hydrogeological assessment (Khan et al, 2022). The potential recharge and movement of an aquifer are influenced by geology, geomorphology, and structures (Butler et al, 2002; Kumar et al, 2022). The geomorphological map created depicts the landforms and topography of a certain area. It is one of the variables that affect groundwater recharge and migration (Githinji et al, 2022). The structural origin, Denudational origin and coastal origin geomorphic features are shown in Fig.4. The Pediment-Pediment complex has assigned the higher value as it covers most of the basin area with gentle slope, which facilitate groundwater recharge. The Moderately Dissected Hills and Valleys having steeper slope ranging from 16 o - 41 o so infiltration of groundwater is possibly least, so the lower weightages is assigned to it. The study of various morphological components is critical for assessing the artificial groundwater recharge zones because it controls groundwater flow on the surface and subsurface (Kumar and Krishna 2016). Slope: Low-lying and gently sloping regions are generally more suitable for artificial groundwater recharge, as they allow increased retention time for surface runoff and enhance infiltration into the subsurface (Rahman et al., 2012; Rajaveni et al., 2017). Numerous studies have shown that slope plays a critical role in recharge potential, with an inverse relationship between gradient and infiltration capacity—steeper slopes lead to faster runoff, higher erosion, and lower recharge efficiency (Khan et al., 2022; Nag et al., 2022; Githinji et al., 2022). Gently sloping areas, in contrast, slow down runoff and provide better conditions for water percolation (Magesh et al., 2011a; Magesh et al., 2011b). In the present study, the slope of the Mochemad River Basin was derived from the 30-meter resolution DEM, and the Natural Breaks (Jenks) classification method was employed to categorize the slope data into four classes: 0°–4°, 4°–9°, 9°–16°, and 17°–42°. This method identifies break points by minimizing the variance within each class while maximizing variance between classes, ensuring a more accurate representation of terrain variability. The overall slope trend in the basin is from northeast to southwest, with a large portion of the area characterized by flat to gently sloping land. These areas (0°–4°) were assigned the highest weight for recharge suitability due to their favorable conditions for water retention and infiltration. Conversely, regions with steep slopes (17°–42°) received the lowest weight, as their high runoff velocity and erosion potential make them unsuitable for groundwater recharge (Mahato et al., 2022). Drainage Density: Drainage density and groundwater recharge are inversely related areas with high drainage density typically allow less time for water infiltration, leading to lower recharge potential, while areas with low drainage density support better water retention and enhanced recharge (Halder et al., 2020; Nag et al., 2022). This negative correlation is well-documented in hydrological studies (Mandal et al., 2016; Luo et al., 2020; Khan et al., 2022). In the present study, drainage networks were initially extracted from the GDEM and further digitized and validated using Survey of India topographic maps for the Mochemad River Basin. To quantify drainage density, the Density tool in ArcGIS was used. The resulting data were classified into five distinct classes using the Natural Breaks (Jenks) classification method, which identifies groupings and patterns in the data by minimizing variance within classes and maximizing variance between them. The five classes identified were: very low (0–0.5 km/km²), low (0.5–1.5 km/km²), moderate (1.5–2.5 km/km²), high (2.5–3.5 km/km²), and very high (3.5–6.5 km/km²). Based on these classifications, recharge suitability rankings were assigned areas with very low and low drainage densities were given higher weights due to their greater infiltration potential, while high and very high drainage density areas received lower weights, reflecting their unsuitability for groundwater recharge. This categorization approach ensures accurate spatial representation of recharge potential, as seen in Figure 6. Soil Type Soil is the most important component in identifying the artificial groundwater recharge zones. Soil-landscape relationship depicting the association of shallow soil in hills (Entisols) and medium soil in narrow valleys (Inceptisols) in the research region (Bhattacharyya et al, 2020). The soil map was created with the assistance of the National Bureau of Soil Survey and Land Use Planning (NBSS & LUP), India, as well as field acquired data. The Mochemad River Basin is predominantly covered by loamy soil, which is observed on undulating slope, at foothill and on the hill. Loamy soil on undulating soil has been assigned the highest rank as it is facilitating more infiltration. While clayey soil has been assigned lowest rank as it clay is impermeable Lineament density Lineaments are linear or curvilinear features on the Earth's surface that reflect underlying structural controls such as joints, fractures, and faults (Luo et al., 2020; Khan et al., 2022). These structural discontinuities enhance the secondary porosity of the host rock, making them significant pathways for groundwater movement and recharge (Haridas et al., 1998; Nag and Saha, 2014). Areas with dense lineament networks are generally more favorable for artificial groundwater recharge due to increased permeability and infiltration potential (Shailaja et al., 2019; Mahato et al., 2022). The Mochemad River Basin, lineaments were extracted from the web-enabled Manual for Geomorphology and Lineament Mapping using Web Map Service (WMS) layers, which were subsequently digitized. The Lineament Density layer was then generated using the Density tool in ArcGIS. The resulting data were classified into five categories using the Natural Breaks (Jenks) classification method, which statistically identifies natural groupings inherent in the data distribution. This method optimizes the classification by reducing within-class variance and maximizing between-class variance (Jenks, 1967). The classified lineament density map (Figure 7) consists of the five categories Very low (0–0.5 km/km²), Low (0.5–1.0 km/km²), Moderate (1.0–1.5 km/km²), High (1.5–2.0 km/km²), and very high (2.0–2.6 km/km²). In the final groundwater recharge zonation, areas with high and very high lineament densities were given higher ranks due to their greater recharge potential, whereas low and very low lineament density areas received lower ranks. This classification provides critical spatial insights for prioritizing zones suitable for artificial recharge interventions. Land Use and Land Cover (LULC) LULC is another key factor that influences the hydro-geological processes and recharge of groundwater (Selvam et al. 2014; Luo et al, 2020). Various LULC patterns of the Mochemad River Basin were derived from LISS III satellite image. The study area has diverse LULC such as Scrubland (7.1 km2), Cropland (35.33 km2), Plantation (48.68 km2), Deciduous Broadleaf Forest (6.23 km2), Mixed Forest (21.27 km2), Evergreen Broadleaf Forest (8.89 km2) and Waterbody (2.7 km2). Weights to each feature in the land use/land cover map can be assigned relative to the water holding capacity (Deepa et al., 2016). The most dominant LULC categories are Cropland and Plantation which are irrigated and considered most suitable area for the groundwater recharge as it favours infiltration of irrigated as well as rainwater (Rejith et al. 2019; Luo et al, 2020). Therefore, Plantation and Cropland have been assigned highest rank while the scrubland which constitutes very small area have been assigned lowest rank. Weight Calculation Using AHP: Saaty (1987) created the AHP, a Multi-Criteria Decision-Making (MCDM) technique that is frequently used to analyse spatial decision problems, such as groundwater issues (Rejith et al., 2019; Zolekar and Bhagat, 2015). The weight of several layers is evaluated using the AHP approach. Using Saaty's scale (1–9) of relative significance, a Pairwise Comparison Matrix (PCM) is initially constructed (values up to 7 were employed for relative importance) (Saaty, 1987) (Table 2 and 3). Table.2. Pairwise Comparison Matrix Criteria Geology Geomorphology Slope Drainage Density Soil Type Lineament Density LULC Geology 1 2 3 4 5 6 7 Geomorphology 0.5 1 2 3 4 5 6 Slope 0.33 0.5 1 2 3 4 5 Drainage Density 0.25 0.33 0.5 1 2 3 4 Soil Type 0.2 0.25 0.33 0.5 1 2 3 Lineament Density 0.16 0.2 0.25 0.33 0.5 1 2 LULC 0.14 0.16 0.2 0.25 0.33 0.5 1 Table.3. Normalized pairwise comparison matrix Criteria Geology Geomorphology Slope Drainage Density Soil Type Lineament Density LULC Geology 0.39 0.45 0.41 0.36 0.32 0.28 0.25 Geomorphology 0.19 0.22 0.27 0.27 0.25 0.23 0.21 Slope 0.13 0.11 0.14 0.18 0.19 0.19 0.18 Drainage Density 0.10 0.07 0.07 0.09 0.13 0.14 0.14 Soil Type 0.08 0.06 0.05 0.05 0.06 0.09 0.11 Lineament Density 0.06 0.04 0.03 0.03 0.03 0.05 0.07 LULC 0.06 0.04 0.03 0.02 0.02 0.02 0.04 The AHP model implemented for the Mochemad River Basin demonstrated a reliable level of internal consistency, with the Consistency Ratio (CR) calculated at 0.02, which is well below the acceptable threshold of 0.1. Upon recalculating, the principal eigenvalue (λ_max) was determined to be 7.21 for a comparison matrix of size n = 7. This yields a Consistency Index (CI) of 0.035, further confirming the logical coherence of the pairwise judgments. Since λ_max exceeds the number of criteria (λ_max > n) and CR remains within acceptable limits (CR < 0.1), the matrix is deemed consistent. This high level of consistency ensures the robustness of the weight derivation process in the AHP framework. Consequently, the AHP technique applied in this study provides a dependable and scientifically sound basis for identifying and classifying artificial groundwater recharge zones in the region. Potential Zones for Groundwater Recharge The delineation of potential groundwater recharge zones in the Mochemad River Basin was done using an integrated geospatial approach that combines Remote Sensing (RS), Geographic Information Systems (GIS), and the Analytical Hierarchy Process (AHP). AHP was employed to assign weights to key hydrogeological and environmental parameters based on their influence on groundwater recharge potential. The thematic layers used in the analysis include Geology, Geomorphology, Slope, Drainage Density, Soil Type, Lineament Density, and Land Use/Land Cover (LULC). Each layer was weighted through a normalized pairwise comparison matrix, calculated by dividing the individual parameter weight by the total weight of all parameters. These weighted layers were overlaid in a GIS environment using a weighted linear combination to generate the composite recharge potential index. The recharge potential map is classified using, the Natural Breaks (Jenks) classification method. This classification technique identifies natural groupings within the dataset by minimizing the variance within each class and maximizing the variance between classes, thus providing a statistically sound categorization. The choice of the Jenks method over other approaches such as equal interval or quantile was due to its superior ability to reflect real-world variations in environmental data (Jenks, 1967; de Smith et al., 2022). The classified map divides the basin into four groundwater recharge suitability zones: Unsuitable (18.29 km²), Moderately Suitable (24.73 km²), Highly Suitable (71.86 km²), and Very Highly Suitable (13.57 km²). These categories help in understanding the spatial distribution of recharge feasibility across the basin. This geospatial modelling approach provides a practical framework for identifying high-priority zones for artificial groundwater recharge interventions in coastal lateritic terrains. The "very highly suitable" zones are typically found in areas with favorable factors such as low slopes, high lineament density, and permeable soils, whereas "unsuitable" zones occur in steep, less permeable, or highly drained areas. The integration of AHP-GIS with the Natural Breaks classification technique results in a robust, data-driven decision-support tool that can guide the construction of location-specific recharge structures such as check dams, percolation tanks, and contour trenches. This study not only addresses the water security challenges of the Mochemad River Basin but also provides a replicable model for other regions facing similar hydrogeological conditions. Table 1 Weights of the criterion used for AHP method Sr. No. Criteria Sub Criteria Normalised Weight Weighted Influence (%) Assigned Weightage 1 Geology Amphibolite 0.35 35 4 Ultramafite 4 BIF 4 Dolerite Dyke 3 Quartz Vein 4 Biotite Gneiss 5 Fluvio- marine deposits 5 Meta Gabbro 3 Meta- pellite 2 Granite Gneiss 3 Laterite 1 TTG Gneiss 5 2 Geomorphology Pediment- Pediplain Complex 0.24 24 5 Younger Coastal Plain 4 Moderately Dissected Lower Plateau 2 Moderately Dissected Hills and Valleys 1 Moderately Dissected Lower Plateau 3 3 Slope 1. (0 o - 4 o ) 0.16 16 5 2. (4 o -9 o ) 4 3. (9 o -16 o ) 3 4. (16 o – 41 o ) 1 4 Drainage Density 1. (0- 0.5 km/km 2 ) 0.1 10 5 2. (0.5 – 1.5 km/km 2 ) 4 3. (1.5 – 2.5km/km 2 ) 3 4. (2.5 – 3.5 km/km 2 ) 2 5. (3.5 – 6.5 km/km 2 ) 1 5 Soil Type Clayey Soil 0.07 7 1 Loamy Soil- Undulating Slope 5 Loamy Soil- Foot hill 2 Loamy Soil- Hills 3 6 Lineament Density 1. (0- 0.5 km/km 2 ) 0.05 5 1 2. (0.5 – 1 km/km 2 ) 2 3. (1 – 1.5km/km 2 ) 3 4. (1.5 – 2 km/km 2 ) 4 5. (2 – 2.6 km/km 2 ) 5 7 LULC Evergreen Broadleaf forest 0.03 3 4 Scrubland 1 Cropland 5 Waterbody 5 Mixed Forest 3 Decidous Broadleaf forest 2 Plantation 5 Table. Areas wise suitability for the recharge zone Categories Area (km 2 ) Percentage Unsuitable 18.29 14.24 Moderately Suitable 24.73 19.25 Highly Suitable 71.86 55.94 Very Highly suitable 13.57 10.56 The artificial groundwater recharge zone map presented in Figure 9 illustrates the spatial distribution of areas suitable for artificial recharge within the Mochemad River Basin. The northeastern and southwestern regions of the basin have been categorized as having low potential for artificial recharge, occupying approximately 14.24% of the total area. These areas encompass villages such as Malgaon, Nhaichiad, Mochemad, Ansur, and Tulas, where the predominant geological formations consist of Laterite and Granite Gneiss. These lithological units are often associated with steeply sloping terrain, which promotes rapid surface runoff and limits water infiltration. Furthermore, the presence of moderately to highly dissected hills, valleys, and lateritic plateaus exacerbates the problem, as the uneven and elevated topography inhibits groundwater percolation. These conditions make these regions less suitable for artificial recharge interventions. In contrast, a significant portion of the basin, particularly the central region, has been classified as Highly Suitable to Very Highly Suitable for artificial groundwater recharge. This area is characterized by gentle to moderate slopes, dense lineament networks, moderate drainage density, and permeable soil and geological formations, which collectively enhance the infiltration capacity and groundwater recharge potential. These favorable conditions suggest that the central part of the Mochemad River Basin holds considerable promise for implementing artificial recharge structures such as percolation tanks, check dams, continuous contour trenches (CCT), and recharge shafts. This zonation is crucial for guiding future groundwater management strategies in the region, ensuring that artificial recharge efforts are optimally located for maximum effectiveness. In this analysis, suitable site for constructing artificial recharge and groundwater conservation structures is identified. Various artificial recharge techniques like surface spreading and construction of check dams, percolation ponds/tanks, bench trenching, contour barriers and surface irrigation can be employed in the demarcated favourable zones. Such structures will lower the surface runoff and enhance the infiltration rate (Bhattacharya, 2010). Validation of Artificial Groundwater Recharge Zones The validation of the delineated artificial groundwater recharge zones was conducted using both field-based and statistical approaches to ensure the robustness and accuracy of the proposed AHP-GIS model. This dual validation strategy strengthens confidence in the model’s applicability for practical groundwater recharge planning. In the field-based validation, groundwater level measurements were obtained from 39 wells distributed throughout the Mochemad River Basin during the post-monsoon season of December 2021. These wells were spatially analyzed in relation to the four classified artificial recharge suitability zones: Unsuitable, Moderately Suitable, Highly Suitable, and Very Highly Suitable. Among the 39 wells, 33 were in areas classified as Highly Suitable for artificial recharge, validating the model's prediction that these zones exhibit favourable recharge potential. The water table depth in these zones ranged from 0.38 meters below ground level (bgl) to 2.83 m bgl, indicating shallow groundwater levels suitable for artificial recharge structures. Three wells were found in Moderately Suitable zones with groundwater depths ranging between 3.35 m bgl to 3.96 m bgl, and three wells were in Unsuitable zones, where water levels exceeded 5 m bgl, indicating deeper and less favorable conditions for recharge interventions. Furthermore, the spatial location of Adeli Dam, a surface water harvesting structure constructed across the Mochemad River, was compared against the generated recharge zone map. The dam was located within the Moderately Suitable recharge zone, which aligns with hydrological planning principles, as areas with moderate infiltration capacity are ideal for constructing surface water harvesting structures such as dams and reservoirs (Mahmoud et al., 2014). This practical alignment supports the functional validity of the recharge zoning model, demonstrating its utility in guiding both groundwater recharge and surface water storage infrastructure planning. In addition to field validation, statistical validation of the model’s predictive performance was carried out using Receiver Operating Characteristic (ROC) curve analysis. A total of 65 well locations were used for this analysis, which were split into training and testing datasets to enhance statistical reliability. Seventy percent of the wells (46 points) were used for training the model, while the remaining 30% (19 points) were employed for testing. The ROC curve was developed by plotting the True Positive Rate (sensitivity) against the False Positive Rate (1 - specificity). This analysis evaluates the ability of the recharge zone model to correctly classify zones that correspond with actual well data. The Area Under the Curve (AUC) was calculated using the following trapezoidal integration formula: , Where: X represents the cumulative area of each recharge zone class Y indicates the cumulative number of wells falling within each zone n is the total number of recharge zone categories (in this case, four) X₁, X₂ and Y₁, Y₂ are successive coordinates used in the integration The AUC value for the Mochemad River Basin recharge zone model was calculated to be 0.86, indicating an 86% accuracy rate (Figure 10). According to standard ROC interpretation criteria, this result places the model in the "Very Good" predictive performance category (0.8–0.9), confirming that the spatial outputs of the AHP-GIS model closely correspond with observed groundwater behavior. When compared to similar studies, the AUC value of 0.84 aligns closely with results from Agarwal & Garg (2015), who reported an AUC of 0.85 using a similar AHP-GIS framework; Ababulgu & Molla (2025) who reported 0.77 in Ethiopia; and Abdo et al. (2024) in Syria who achieved 0.877 using 74 validation wells. Kodihal & Akhtar (2024) also achieved 0.90 using an AHP-OWA hybrid in a future-scenario-based analysis for Jaipur. This statistically robust outcome, combined with the practical field validation, affirms that the methodology used is not only scientifically sound but also of practical relevance to water resource planners, engineers, and policymakers. Conclusion This study presents a comprehensive assessment of artificial groundwater recharge zones (AGRZ) in the Mochemad River Basin, a coastal region of Maharashtra characterized by high monsoonal rainfall, lateritic terrain, and increasing vulnerability to saline water intrusion. By integrating Remote Sensing (RS), Geographic Information Systems (GIS), and the Analytical Hierarchy Process (AHP), the basin was classified into four recharge suitability zones—unsuitable (14.24%), moderately suitable (19.25%), highly suitable (55.94%), and very highly suitable (10.56%). The key finding is that approximately 66.5% of the area falls under highly to very highly suitable zones for groundwater recharge. The use of multi-thematic layers such as lithology, slope, land use/land cover, drainage density, and lineament density provided a geospatially robust foundation for accurate classification. The ROC-AUC value of 84.6% confirms the reliability of the AHP-GIS model, further supported by field-based groundwater level data which correlated well with the spatial model outputs. The novelty of this research lies in its application of a decision-support AHP-GIS framework to a humid tropical coastal basin, a context seldom explored in recharge zone modeling. Most previous studies have concentrated on arid or semi-arid and basaltic regions. Here, the methodology addresses the unique challenges of lateritic soil, steep-slope erosion, and saline intrusion, offering a valuable precedent for similar coastal settings globally. The study also provides practical recommendations, proposing suitable recharge structures such as check dams, percolation tanks, Continuous Contour Trenches (CCT), and Mati Nala Bandharas (MNB), tailored to site-specific geomorphic and hydrological conditions. These outputs can directly aid water resource planners, engineers, and policymakers in designing and prioritizing interventions for groundwater conservation. However, several limitations were identified. The model uses post-monsoon groundwater level data, which, although effective in indicating immediate recharge, may not capture the full seasonal variability. Additionally, the moderate resolution of input datasets and lack of long-term groundwater monitoring could impact the spatial precision of results. The study also assumes static land use and climate conditions, which may not hold in the face of rapid land development or climate change. In conclusion, this research provides a replicable, low-cost, and efficient spatial planning model for identifying groundwater recharge zones in coastal and lateritic regions. Future studies are recommended to incorporate multi-seasonal water level data, employ higher-resolution datasets, and integrate climate change projections to enhance temporal and spatial accuracy. This work lays a foundation for sustainable water management strategies in ecologically sensitive and data-scarce coastal basins. Declarations Author Contribution: Tejas S. Naik (Corresponding Author): Collects data, performs GIS-based analysis, conducts Analytical Hierarchy Process (AHP) modelling and statistical analysis. Satyajit K. Gaikwad: Supervises the research, reviews the manuscript, and provides critical revisions and improvements. Sneha Sawant: Interprets hydrogeological parameters, prepares thematic maps and validates recharge potential zones. Praveen N. Kamble: Assists in data processing, cartographic visualization, and manuscript drafting. Funding : This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Clinical trial number: Not applicable. Data availability: The datasets generated and/or analysed during the current study are available from the corresponding author on reasonable request. Consent to Publish declaration : Not applicable Consent to Participate declaration: Not applicable Acknowledgements: The authors wish to thank all who assisted in conducting this work. Conflict of interest: The authors declare no financial or non-financial interests exist that are directly or indirectly related to this work. Ethics declaration: The authors confirm that all the research meets ethical guidelines. References Githinji, T. W., Dindi, E. W., Kuria, Z. N., & Olago, D. O. (2022). Application of analytical hierarchy process and integrated fuzzy-analytical hierarchy process for mapping potential groundwater recharge zone using GIS in the arid areas of Ewaso Ng'iro–Lagh Dera Basin, Kenya. HydroResearch , 5 , 22-34. Rajasekhar, M., Gadhiraju, S. R., Kadam, A., & Bhagat, V. (2020). 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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-9140855","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":620083520,"identity":"789441bd-2cae-4882-a3a7-a556a93e9203","order_by":0,"name":"Tejas 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Map\u003c/p\u003e","description":"","filename":"floatimage8.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9140855/v1/8ff0eb29c1394b0e4bb7dfa1.jpeg"},{"id":106778645,"identity":"d0ef6ce1-b33c-40bd-a963-bdba6ef14aa2","added_by":"auto","created_at":"2026-04-13 11:13:37","extension":"jpeg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":97395,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 7LULC Map\u003c/p\u003e","description":"","filename":"floatimage9.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9140855/v1/1b4a4d9c77cb3972a5b091fd.jpeg"},{"id":106778642,"identity":"e9c68fd6-0d77-4bd4-a037-da0f17509e8b","added_by":"auto","created_at":"2026-04-13 11:13:37","extension":"jpeg","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":533352,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 8 Artificial Groundwater Recharge Zone Map\u003c/p\u003e","description":"","filename":"floatimage10.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9140855/v1/7a21980da83c286d2dc6c406.jpeg"},{"id":106778630,"identity":"9eb73144-6473-4eb9-ae6c-c1e49f1bec95","added_by":"auto","created_at":"2026-04-13 11:13:29","extension":"jpeg","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":295815,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 9 Artificial surface storage and groundwater recharge structures map\u003c/p\u003e","description":"","filename":"floatimage11.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9140855/v1/7280852678929cbea1df5311.jpeg"},{"id":106994200,"identity":"81273ab0-568b-4d5c-b61b-84507cacbc41","added_by":"auto","created_at":"2026-04-15 15:06:17","extension":"jpeg","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":2020944,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 10 Receiver Operating Characteristic (ROC) curve for groundwater potential map validation.\u003c/p\u003e","description":"","filename":"floatimage12.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9140855/v1/d28e2e9e25d626eec0e64db7.jpeg"},{"id":106994952,"identity":"aa2910c3-f95a-4689-9042-51e3137b8b5e","added_by":"auto","created_at":"2026-04-15 15:20:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4620223,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9140855/v1/6a7fe22e-30a2-4951-9869-0d576dba7b9e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Geospatial Modelling and Multi-Criteria Evaluation of Groundwater Recharge Potential in a Climate-Stressed Coastal Basin of Western India","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGroundwater is an indispensable freshwater resource supporting domestic, agricultural, and industrial activities across the globe. It constitutes nearly 95% of the planet\u0026rsquo;s available freshwater (Oksana and Dmytro \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), and its sustainable management is crucial, especially in regions facing growing water stress due to climatic variability, population growth, and urban expansion (Kumari and Singh, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Yazdi et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Coastal regions face unique challenges such as over-extraction of groundwater and subsequent seawater intrusion, which compromises freshwater availability and quality (Bhattacharya, 2010; Rajasekhar et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Howlader et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This concern is evident in the southern Konkan region, including the Mochemad River Basin, which has shown declining groundwater levels over decadal timescales (Das, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Gaikwad et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Artificial groundwater recharge (AGR) is recognized as a sustainable and effective solution to mitigate groundwater depletion and seawater intrusion in coastal aquifers (Pitchaimani et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Hasan et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The integration of geospatial technologies with decision-support models such as the Analytical Hierarchical Process (AHP) has proven successful for mapping groundwater recharge potential zones (Saaty, 1980; Patel et al., 2022; Zghibi et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). AHP enables systematic evaluation of multiple spatial factors\u0026mdash;including geology, slope, drainage density, lineament density, and land use\u0026mdash;by assigning relative weights to reflect their influence on recharge (Saaty, 1988; Kaliraj et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRecent studies by Ali et al. (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2024\u003c/span\u003ea) have demonstrated the effectiveness of machine learning ensemble models for delineating groundwater potential zones in varied hydrogeological settings like the Lidder watershed, India, providing a valuable comparison for evaluating model accuracy and geospatial integration techniques. Similarly, Ali et al. (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2024\u003c/span\u003eb) have emphasized watershed prioritization using morphometric analysis, reinforcing the importance of topographic and drainage parameters in recharge modelling\u0026mdash;a key aspect also addressed in this study. In addition, Ali et al. (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) assessed climate change trends using non-parametric methods and machine learning, underscoring the need to integrate such insights when dealing with dynamic recharge conditions in climate-sensitive coastal regions. These studies have demonstrated the effectiveness of hybrid approaches combining AHP and remote sensing/GIS in a variety of geohydrological contexts. For instance, Khan et al. (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) applied AHP and GIS in Saudi Arabia\u0026rsquo;s arid environments to delineate aquifer recharge zones with high predictive accuracy. Similarly, Saha et al. (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) utilized AHP for the Deccan basaltic region of India, reinforcing the utility of this method in complex volcanic terrains. Sathiyamoorthy et al. (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) highlighted how AHP-GIS integration in coastal Tamil Nadu can inform sustainable recharge strategies by accounting for regional geomorphology and land use. These studies provide essential frameworks that validate the method's applicability across diverse hydrogeological settings.\u003c/p\u003e \u003cp\u003eDespite these advancements, limited studies have focused specifically on high-rainfall coastal environments like the Mochemad River Basin, which experiences over 3500 mm of annual precipitation yet suffers from saline intrusion and groundwater scarcity post-monsoon. Moreover, no prior attempt has been made to model artificial recharge zones in this basin using AHP-GIS techniques. The present study addresses this gap by developing a spatially explicit recharge suitability model based on high-resolution geospatial datasets and validated through ROC\u0026ndash;AUC analysis. The novelty of this research lies in its application of a multi-criteria AHP-based approach tailored to the hydrogeological complexity of a coastal river basin in the Western Ghats foothills. The study identifies site-specific recharge structures such as check dams, percolation tanks, and continuous contour trenches, providing practical guidance for sustainable groundwater management. This framework can be adapted for similar coastal and lateritic terrains vulnerable to saline intrusion and seasonal water shortages.\u003c/p\u003e\n\u003ch3\u003eStudy Area\u003c/h3\u003e\n\u003cp\u003eThe present study focuses on the Mochemad River Basin located in the Sindhudurg district, part of the southern Konkan region along the west coast of India. The basin spans across Kudal, Sawantwadi, and Vengurla tehsils and is geographically bounded by longitudes 73\u0026deg;39\u0026prime; to 73\u0026deg;49\u0026prime; E and latitudes 15\u0026deg;47\u0026prime; N to 15\u0026deg;57\u0026prime; N. It is represented on the Survey of India Toposheet Nos. 48 E/9 and 48 E/13 (scale 1:50,000). The Mochemad River originates at Humras village (elevation 131 m) in Kudal and flows southwest, draining into the Arabian Sea near Tak village. The river has a total length of 28 km and a basin area of approximately 130 km\u0026sup2;.\u003c/p\u003e \u003cp\u003eThis coastal basin experiences high annual rainfall, ranging between 3000 mm and 4700 mm (Bandaru et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), with an average of 3070 mm (CGWB, 2014). Despite this, the region frequently faces post-monsoon water scarcity due to rapid surface runoff, limited aquifer storage, and saline water intrusion. The basin's physiography is marked by flat-topped hills and lateritic plateaus in the east and coastal plains in the west (CGWB, 2014), which greatly influence groundwater behaviour.\u003c/p\u003e \u003cp\u003eThese hydrogeological conditions underscore the necessity of modelling and mapping artificial groundwater recharge zones to manage and conserve groundwater resources effectively. The main aquifer formations are laterites, granites, and granitic gneisses, with groundwater occurring in unconfined aquifers at shallow depths (2\u0026ndash;10 m bgl). Dug and bore wells in coastal alluvium typically yield 2\u0026ndash;5 m\u0026sup3;/day, while borewells in the gneissic complex are deeper (50\u0026ndash;70 m) and yield 500\u0026ndash;7770 LPH. Laterite aquifers, prevalent in the northeast and south of the basin, exhibit specific capacities between 79.10 and 424.57 LPM/m drawdown, transmissivities of 46.59\u0026ndash;375.22 m\u0026sup2;/day, and permeabilities ranging from 7.40 to 425.22 m/day (CGWB, 2014). Given the complex hydrogeological and physiographic settings, coupled with seasonal variability, spatial modeling using GIS and AHP offers a powerful approach to delineate suitable sites for artificial recharge. This study aims to develop such a spatially explicit model tailored to the unique terrain of the Mochemad River Basin.\u003c/p\u003e "},{"header":"Method and Material","content":"\u003cp\u003eThe multi-parametric analysis for demarcating artificial groundwater recharge areas of Mochemad watershed has been done using AHP technique in GIS environment. The current study is implemented in the following methods (Fig.2). In the study of the Mochemad River Basin, seven criterions such as Geology (GG), Geomorphology (GM), Drainage Density (DD), Slope (S), Lineament density (LD), Soil type and LULC have been analysed by AHP approach using normalized weight to demarcate area for artificial groundwater recharge. Seven spatial criterions like Geology (GG), Geomorphology (GM), Drainage Density (DD), Slope (S), Lineament density (LD), Soil type and LULC have been used for the preparation of geospatial database. Lithological and geomorphological map was prepared using district resource map of Sindhudurg district. The Survey of India (SOI) toposheet, from which the drainage density layer was created, was used to digitise the stream network. To create a slope map, SRTM DEM was taken from US Geological Survey Earth Explorer and processed. The \u0026quot;Manual for Geomorphology and Lineament mapping (web version)\u0026quot; was used to digitise the lineament map, which was then processed to determine its density. LISS 3 satellite imagery was used to create the Land Use and Land Cover (LULC) map, and the National Bureau of Soil Survey and Land Use Planning (NBSS and LUP, Nagpur) map was used to create the soil type map layer (Reshmidevi et al., 2008).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable.1: Details of the data used for the study their source\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eData Type\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 321px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSources\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eData used for\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003eTopographical maps No.\u003c/p\u003e\n \u003cp\u003e48 E/9 and 48 E/13\u003c/p\u003e\n \u003cp\u003e(Scale 1:50,000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 321px;\"\u003e\n \u003cp\u003eSurvey of India (SOI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eDrainage map\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003eGEDM\u003c/p\u003e\n \u003cp\u003e(Resolution= 30m)\u003c/p\u003e\n \u003cp\u003e(Scale 1:50,000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 321px;\"\u003e\n \u003cp\u003eGlovis\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eRelief, slope, drainage, topographic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003eGeological map\u003c/p\u003e\n \u003cp\u003e(Scale 1:50,000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 321px;\"\u003e\n \u003cp\u003eGSI District Resource Map,\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eGeology\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003eGeomorphological map\u003c/p\u003e\n \u003cp\u003e(Scale 1:2,50,000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 321px;\"\u003e\n \u003cp\u003eBHUVAN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eGeomorphology\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003eLULC Data\u003c/p\u003e\n \u003cp\u003e(Scale 1:50,000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 321px;\"\u003e\n \u003cp\u003eLISS- III satellite imagery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eLULC Map\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003eSoil data\u003c/p\u003e\n \u003cp\u003e(Scale 1:2,50,000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 321px;\"\u003e\n \u003cp\u003eNational Bureau of Soil Survey and land Use Planning\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eSoil type\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003eLineament Data\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(Scale 1:50,000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 321px;\"\u003e\n \u003cp\u003eToposheet, GSI map, and GEDM (as supplementary)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eLineament density and map.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003eGroundwater fluctuation data\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 321px;\"\u003e\n \u003cp\u003eField data\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eGroundwater level\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eMulti- criteria Decision making using Analytical Hierarchical process (AHP)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study of Mochemad River Basin, AHP is utilized to demarcate the regions for artificial groundwater recharge. The method was suggested by Saaty (1980) for solving complicated decision-making issues. Field study and experts view was used to assign the weight of the Saaty\u0026rsquo;s 1-9 scale for each thematic layer. The response of these influencing parameters is weighted as per their reaction to recharge of groundwater. A factor with high rank is the layer with high impact and factor with low rank are with low impact on the groundwater recharge. AHP was used for assigning the weights and calculate the normalized weights for the parameters influencing groundwater recharge. The Pairwise Comparison Matrix of thematic layers such as Geology, Geomorphology, Slope, Drainage Density, Soil Type, Lineament Density and LULC were compared according to the 1-9 scale suggested by Saaty (2008). The consistency ratio (CR) and consistency index (CI) values were computed to examine the reliability of the obtained results. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe formula used is\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"139\" height=\"79\"\u003e\n \u003cv:shapetype id=\"_x0000_t75\" coordsize=\"21600,21600\" o:spt=\"75\" o:preferrelative=\"t\" path=\"m@4@5l@4@11@9@11@9@5xe\" filled=\"f\" stroked=\"f\"\u003e\n \u003cv:stroke joinstyle=\"miter\"\u003e\n \u003cv:formulas\u003e\n \u003cv:f eqn=\"if lineDrawn pixelLineWidth 0\"\u003e\n \u003cv:f eqn=\"sum @0 1 0\"\u003e\n \u003cv:f eqn=\"sum 0 0 @1\"\u003e\n \u003cv:f eqn=\"prod @2 1 2\"\u003e\u0026nbsp;\u003cv:f eqn=\"prod @3 21600 pixelWidth\"\u003e\u0026nbsp;\u003cv:f eqn=\"prod @3 21600 pixelHeight\"\u003e\u0026nbsp;\u003cv:f eqn=\"sum @0 0 1\"\u003e\u0026nbsp;\u003cv:f eqn=\"prod @6 1 2\"\u003e\u0026nbsp;\u003cv:f eqn=\"prod @7 21600 pixelWidth\"\u003e\u0026nbsp;\u003cv:f eqn=\"sum @8 21600 0\"\u003e\u0026nbsp;\u003cv:f eqn=\"prod @7 21600 pixelHeight\"\u003e\u0026nbsp;\u003cv:f eqn=\"sum @10 21600 0\"\u003e\u0026nbsp;\u003c/v:f\u003e\n \u003c/v:f\u003e\n \u003c/v:f\u003e\n \u003c/v:f\u003e\n \u003c/v:f\u003e\n \u003c/v:f\u003e\n \u003c/v:f\u003e\n \u003c/v:f\u003e\n \u003c/v:f\u003e\n \u003c/v:f\u003e\n \u003c/v:f\u003e\n \u003c/v:f\u003e\n \u003c/v:formulas\u003e\n \u003cv:path o:extrusionok=\"f\" gradientshapeok=\"t\" o:connecttype=\"rect\"\u003e\u0026nbsp;\u003c/v:path\u003e\n \u003c/v:stroke\u003e\n \u003c/v:shapetype\u003e\n \u003cv:shape id=\"_x0000_i1025\" type=\"#_x0000_t75\"\u003e\u0026nbsp;\u003cv:imagedata src=\"file:///C%3A/Users/pgs9865/AppData/Local/Temp/msohtmlclip1/01/clip_image001.png\" o:title=\"\" chromakey=\"white\"\u003e\u0026nbsp;\u003c/v:imagedata\u003e\n \u003c/v:shape\u003e\n\u003c/p\u003e\n\u003cp\u003eWhere CR is consistency ratio, RI is random consistency index whose values are derived from the order of matrix. CI is consistency index which is calculated from the formula given below\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"132\" height=\"64\"\u003e\u003c/p\u003e\n\u003cp\u003eWhere \u0026lambda; is the principal Eigen value of the matrix and n is the number of parameters affecting groundwater recharge. The CR should be less than 0.1 to avoid the inconsistency.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGroundwater Level Data and Model Validation Approach\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe output map validation is done by using post-monsoon groundwater level data. The rationale behind selecting post-monsoon data lies in its effectiveness in representing the immediate recharge response of aquifers following the monsoon rainfall. During this period, water levels are typically at their peak, and the recharge from both natural infiltration and potential artificial recharge structures is most evident. This makes post-monsoon data a more reliable indicator for evaluating the effectiveness of recharge-prone zones delineated in the model. In contrast, pre-monsoon data often reflect seasonal depletion influenced by prolonged extraction and evapotranspiration, offering limited insight into the recharge potential or capacity of the terrain. Therefore, the post-monsoon period provides a more consistent and representative baseline for validating the spatial accuracy and functionality of the artificial groundwater recharge zone (AGRZ) model. This approach is consistent with other hydrological studies that assess recharge zones based on water level rise post-monsoon (e.g., Rajaveni et al., 2017; Choudhury et al., 2023).\u003c/p\u003e"},{"header":"Result and Discussion","content":"\u003cp\u003e\u003cstrong\u003eGeology:\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe porosity and permeability of rocks determine the presence and flow of groundwater (Balaji et al. 2019; Luo et al, 2020; Khan et al, 2022). There is a variety of lithology in the research region (Deendar, 2003). Eleven distinct geological units, ranging in age from Pre-Cambrian to modern, make up the river basin under study (Gaikwad et al., 2020). A suite of Tonalite, Trondjhemite, and Granodiorite (TTG) gneisses are mostly exposed, together with granitoids and migmatites that contain enclaves of Banded Iron Formation (BIF), Amphibolite, and Ultramafite. Dolerite dykes and quartz veins penetrate the biotite TTG, which is the basement for the supracrustal rocks in the region, such as Metapellite and BIF. Laterite is developed over the country rock at higher elevations and fluvio- marine sediments are present near the mouth of the river (GSI, 2012). The homogeneously foliated Biotite-TTG gneiss and fluvio-marine deposits demonstrates high infiltration capacity and hence given highest rank (Abijith et al. 2020; Mahato et al, 2022). Both primary and secondary laterite are found in large quantities in low-lying platens and along the Western coast. They are porous, although their impermeability will limit infiltration. As a result, it has the lowest ranking.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGeomorphology:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGeomorphology is the most significant characteristic for hydrogeological assessment (Khan et al, 2022). The potential recharge and movement of an aquifer are influenced by geology, geomorphology, and structures (Butler et al, 2002; Kumar et al, 2022). The geomorphological map created depicts the landforms and topography of a certain area. It is one of the variables that affect groundwater recharge and migration (Githinji et al, 2022). The structural origin, Denudational origin and coastal origin geomorphic features are shown in Fig.4. The Pediment-Pediment complex has assigned the higher value as it covers most of the basin area with gentle slope, which facilitate groundwater recharge. The Moderately Dissected Hills and Valleys having steeper slope ranging from 16\u003csup\u003eo\u003c/sup\u003e- 41\u003csup\u003eo\u003c/sup\u003e so infiltration of groundwater is possibly least, so the lower weightages is assigned to it. The study of various morphological components is critical for assessing the artificial groundwater recharge zones because it controls groundwater flow on the surface and subsurface (Kumar and Krishna 2016).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSlope:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLow-lying and gently sloping regions are generally more suitable for artificial groundwater recharge, as they allow increased retention time for surface runoff and enhance infiltration into the subsurface (Rahman et al., 2012; Rajaveni et al., 2017). Numerous studies have shown that slope plays a critical role in recharge potential, with an inverse relationship between gradient and infiltration capacity\u0026mdash;steeper slopes lead to faster runoff, higher erosion, and lower recharge efficiency (Khan et al., 2022; Nag et al., 2022; Githinji et al., 2022). Gently sloping areas, in contrast, slow down runoff and provide better conditions for water percolation (Magesh et al., 2011a; Magesh et al., 2011b).\u003c/p\u003e\n\u003cp\u003eIn the present study, the slope of the Mochemad River Basin was derived from the 30-meter resolution DEM, and the Natural Breaks (Jenks) classification method was employed to categorize the slope data into four classes: 0\u0026deg;\u0026ndash;4\u0026deg;, 4\u0026deg;\u0026ndash;9\u0026deg;, 9\u0026deg;\u0026ndash;16\u0026deg;, and 17\u0026deg;\u0026ndash;42\u0026deg;. This method identifies break points by minimizing the variance within each class while maximizing variance between classes, ensuring a more accurate representation of terrain variability.\u003c/p\u003e\n\u003cp\u003eThe overall slope trend in the basin is from northeast to southwest, with a large portion of the area characterized by flat to gently sloping land. These areas (0\u0026deg;\u0026ndash;4\u0026deg;) were assigned the highest weight for recharge suitability due to their favorable conditions for water retention and infiltration. Conversely, regions with steep slopes (17\u0026deg;\u0026ndash;42\u0026deg;) received the lowest weight, as their high runoff velocity and erosion potential make them unsuitable for groundwater recharge (Mahato et al., 2022).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDrainage Density:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDrainage density and groundwater recharge are inversely related areas with high drainage density typically allow less time for water infiltration, leading to lower recharge potential, while areas with low drainage density support better water retention and enhanced recharge (Halder et al., 2020; Nag et al., 2022). This negative correlation is well-documented in hydrological studies (Mandal et al., 2016; Luo et al., 2020; Khan et al., 2022).\u003c/p\u003e\n\u003cp\u003eIn the present study, drainage networks were initially extracted from the GDEM and further digitized and validated using Survey of India topographic maps for the Mochemad River Basin. To quantify drainage density, the Density tool in ArcGIS was used. The resulting data were classified into five distinct classes using the Natural Breaks (Jenks) classification method, which identifies groupings and patterns in the data by minimizing variance within classes and maximizing variance between them. The five classes identified were: very low (0\u0026ndash;0.5 km/km\u0026sup2;), low (0.5\u0026ndash;1.5 km/km\u0026sup2;), moderate (1.5\u0026ndash;2.5 km/km\u0026sup2;), high (2.5\u0026ndash;3.5 km/km\u0026sup2;), and very high (3.5\u0026ndash;6.5 km/km\u0026sup2;).\u003c/p\u003e\n\u003cp\u003eBased on these classifications, recharge suitability rankings were assigned areas with very low and low drainage densities were given higher weights due to their greater infiltration potential, while high and very high drainage density areas received lower weights, reflecting their unsuitability for groundwater recharge. This categorization approach ensures accurate spatial representation of recharge potential, as seen in Figure 6.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSoil Type\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Soil is the most important component in identifying the artificial groundwater recharge zones. Soil-landscape relationship depicting the association of shallow soil in hills (Entisols) and medium soil in narrow valleys (Inceptisols) in the research region (Bhattacharyya et al, 2020). The soil map was created with the assistance of the National Bureau of Soil Survey and Land Use Planning (NBSS \u0026amp; LUP), India, as well as field acquired data. The Mochemad River Basin is predominantly covered by loamy soil, which is observed on undulating slope, at foothill and on the hill. Loamy soil on undulating soil has been assigned the highest rank as it is facilitating more infiltration. While clayey soil has been assigned lowest rank as it clay is impermeable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLineament density\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLineaments are linear or curvilinear features on the Earth\u0026apos;s surface that reflect underlying structural controls such as joints, fractures, and faults (Luo et al., 2020; Khan et al., 2022). These structural discontinuities enhance the secondary porosity of the host rock, making them significant pathways for groundwater movement and recharge (Haridas et al., 1998; Nag and Saha, 2014). Areas with dense lineament networks are generally more favorable for artificial groundwater recharge due to increased permeability and infiltration potential (Shailaja et al., 2019; Mahato et al., 2022).\u003c/p\u003e\n\u003cp\u003eThe Mochemad River Basin, lineaments were extracted from the web-enabled Manual for Geomorphology and Lineament Mapping using Web Map Service (WMS) layers, which were subsequently digitized. The Lineament Density layer was then generated using the Density tool in ArcGIS. The resulting data were classified into five categories using the Natural Breaks (Jenks) classification method, which statistically identifies natural groupings inherent in the data distribution. This method optimizes the classification by reducing within-class variance and maximizing between-class variance (Jenks, 1967).\u003c/p\u003e\n\u003cp\u003eThe classified lineament density map (Figure 7) consists of the five categories Very low (0\u0026ndash;0.5 km/km\u0026sup2;), Low (0.5\u0026ndash;1.0 km/km\u0026sup2;), Moderate (1.0\u0026ndash;1.5 km/km\u0026sup2;), High (1.5\u0026ndash;2.0 km/km\u0026sup2;), and very high (2.0\u0026ndash;2.6 km/km\u0026sup2;). In the final groundwater recharge zonation, areas with high and very high lineament densities were given higher ranks due to their greater recharge potential, whereas low and very low lineament density areas received lower ranks. This classification provides critical spatial insights for prioritizing zones suitable for artificial recharge interventions.\u003c/p\u003e\n\u003cp\u003eLand Use and Land Cover (LULC)\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;LULC is another key factor that influences the hydro-geological processes and recharge of groundwater (Selvam et al. 2014; Luo et al, 2020). Various LULC patterns of the Mochemad River Basin were derived from LISS III satellite image. The study area has diverse LULC such as Scrubland (7.1 km2), Cropland (35.33 km2), Plantation (48.68 km2), Deciduous Broadleaf Forest (6.23 km2), Mixed Forest (21.27 km2), Evergreen Broadleaf Forest (8.89 km2) and Waterbody (2.7 km2).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWeights to each feature in the land use/land cover map can be assigned relative to the water holding capacity (Deepa et al., 2016). The most dominant LULC categories are Cropland and Plantation which are irrigated and considered most suitable area for the groundwater recharge as it favours infiltration of irrigated as well as rainwater (Rejith et al. 2019; Luo et al, 2020). Therefore, Plantation and Cropland have been assigned highest rank while the scrubland which constitutes very small area have been assigned lowest rank.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWeight Calculation Using AHP:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSaaty (1987) created the AHP, a Multi-Criteria Decision-Making (MCDM) technique that is frequently used to analyse spatial decision problems, such as groundwater issues (Rejith et al., 2019; Zolekar and Bhagat, 2015). The weight of several layers is evaluated using the AHP approach. Using Saaty\u0026apos;s scale (1\u0026ndash;9) of relative significance, a Pairwise Comparison Matrix (PCM) is initially constructed (values up to 7 were employed for relative importance) (Saaty, 1987) (Table 2 and 3).\u003c/p\u003e\n\u003cp\u003eTable.2.\u0026nbsp;Pairwise Comparison Matrix\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"637\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 112px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCriteria\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGeology\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 112px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGeomorphology\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 51px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSlope\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDrainage Density\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSoil Type\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLineament Density\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLULC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 112px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGeology\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 87px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 112px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 51px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 79px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 63px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 81px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 53px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 112px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGeomorphology\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 112px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 51px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 79px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 63px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 81px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 53px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 112px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSlope\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 112px;\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 51px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 79px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 63px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 81px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 53px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 112px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDrainage Density\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 112px;\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 79px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 63px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 81px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 53px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 112px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSoil Type\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 112px;\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 79px;\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 63px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 81px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 53px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 112px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLineament Density\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 112px;\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 79px;\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 63px;\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 81px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 53px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 112px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLULC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 112px;\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 79px;\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 63px;\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 81px;\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 53px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable.3. Normalized pairwise comparison matrix\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"625\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 107px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCriteria\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 93px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGeology\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 109px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGeomorphology\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 50px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSlope\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDrainage Density\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSoil Type\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLineament Density\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 51px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLULC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 107px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGeology\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 93px;\"\u003e\n \u003cp\u003e0.39\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 109px;\"\u003e\n \u003cp\u003e0.45\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 50px;\"\u003e\n \u003cp\u003e0.41\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 77px;\"\u003e\n \u003cp\u003e0.36\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.32\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0.25\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 107px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGeomorphology\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 93px;\"\u003e\n \u003cp\u003e0.19\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 109px;\"\u003e\n \u003cp\u003e0.22\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 50px;\"\u003e\n \u003cp\u003e0.27\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 77px;\"\u003e\n \u003cp\u003e0.27\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.25\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0.21\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 107px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSlope\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 93px;\"\u003e\n \u003cp\u003e0.13\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 109px;\"\u003e\n \u003cp\u003e0.11\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 50px;\"\u003e\n \u003cp\u003e0.14\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 77px;\"\u003e\n \u003cp\u003e0.18\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.19\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0.18\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 107px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDrainage Density\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 93px;\"\u003e\n \u003cp\u003e0.10\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 109px;\"\u003e\n \u003cp\u003e0.07\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 50px;\"\u003e\n \u003cp\u003e0.07\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 77px;\"\u003e\n \u003cp\u003e0.09\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.13\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0.14\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 107px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSoil Type\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 93px;\"\u003e\n \u003cp\u003e0.08\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 109px;\"\u003e\n \u003cp\u003e0.06\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 50px;\"\u003e\n \u003cp\u003e0.05\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 77px;\"\u003e\n \u003cp\u003e0.05\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.06\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0.11\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 107px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLineament Density\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 93px;\"\u003e\n \u003cp\u003e0.06\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 109px;\"\u003e\n \u003cp\u003e0.04\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 50px;\"\u003e\n \u003cp\u003e0.03\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 77px;\"\u003e\n \u003cp\u003e0.03\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.03\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0.07\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 107px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLULC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 93px;\"\u003e\n \u003cp\u003e0.06\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 109px;\"\u003e\n \u003cp\u003e0.04\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 50px;\"\u003e\n \u003cp\u003e0.03\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 77px;\"\u003e\n \u003cp\u003e0.02\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.02\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0.04\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe AHP model implemented for the Mochemad River Basin demonstrated a reliable level of internal consistency, with the Consistency Ratio (CR) calculated at 0.02, which is well below the acceptable threshold of 0.1. Upon recalculating, the principal eigenvalue (\u0026lambda;_max) was determined to be 7.21 for a comparison matrix of size n = 7. This yields a Consistency Index (CI) of 0.035, further confirming the logical coherence of the pairwise judgments. Since \u0026lambda;_max exceeds the number of criteria (\u0026lambda;_max \u0026gt; n) and CR remains within acceptable limits (CR \u0026lt; 0.1), the matrix is deemed consistent. This high level of consistency ensures the robustness of the weight derivation process in the AHP framework. Consequently, the AHP technique applied in this study provides a dependable and scientifically sound basis for identifying and classifying artificial groundwater recharge zones in the region.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePotential Zones for Groundwater Recharge\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe delineation of potential groundwater recharge zones in the Mochemad River Basin was done using an integrated geospatial approach that combines Remote Sensing (RS), Geographic Information Systems (GIS), and the Analytical Hierarchy Process (AHP). AHP was employed to assign weights to key hydrogeological and environmental parameters based on their influence on groundwater recharge potential. The thematic layers used in the analysis include Geology, Geomorphology, Slope, Drainage Density, Soil Type, Lineament Density, and Land Use/Land Cover (LULC). Each layer was weighted through a normalized pairwise comparison matrix, calculated by dividing the individual parameter weight by the total weight of all parameters. These weighted layers were overlaid in a GIS environment using a weighted linear combination to generate the composite recharge potential index.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe recharge potential map is classified using, the Natural Breaks (Jenks) classification method. This classification technique identifies natural groupings within the dataset by minimizing the variance within each class and maximizing the variance between classes, thus providing a statistically sound categorization. The choice of the Jenks method over other approaches such as equal interval or quantile was due to its superior ability to reflect real-world variations in environmental data (Jenks, 1967; de Smith et al., 2022). The classified map divides the basin into four groundwater recharge suitability zones: Unsuitable (18.29 km\u0026sup2;), Moderately Suitable (24.73 km\u0026sup2;), Highly Suitable (71.86 km\u0026sup2;), and Very Highly Suitable (13.57 km\u0026sup2;). These categories help in understanding the spatial distribution of recharge feasibility across the basin.\u003c/p\u003e\n\u003cp\u003eThis geospatial modelling approach provides a practical framework for identifying high-priority zones for artificial groundwater recharge interventions in coastal lateritic terrains. The \u0026quot;very highly suitable\u0026quot; zones are typically found in areas with favorable factors such as low slopes, high lineament density, and permeable soils, whereas \u0026quot;unsuitable\u0026quot; zones occur in steep, less permeable, or highly drained areas. The integration of AHP-GIS with the Natural Breaks classification technique results in a robust, data-driven decision-support tool that can guide the construction of location-specific recharge structures such as check dams, percolation tanks, and contour trenches. This study not only addresses the water security challenges of the Mochemad River Basin but also provides a replicable model for other regions facing similar hydrogeological conditions.\u003c/p\u003e\n\u003cp\u003eTable 1 Weights of the criterion used for AHP method\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"660\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSr. No.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCriteria\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 216px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSub Criteria\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNormalised Weight\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWeighted Influence (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAssigned Weightage\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" rowspan=\"12\" style=\"width: 51px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" rowspan=\"12\" style=\"width: 108px;\"\u003e\n \u003cp\u003eGeology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 216px;\"\u003e\n \u003cp\u003eAmphibolite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" rowspan=\"12\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" rowspan=\"12\" style=\"width: 103px;\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 216px;\"\u003e\n \u003cp\u003eUltramafite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 216px;\"\u003e\n \u003cp\u003eBIF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 216px;\"\u003e\n \u003cp\u003eDolerite Dyke\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 216px;\"\u003e\n \u003cp\u003eQuartz Vein\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 216px;\"\u003e\n \u003cp\u003eBiotite Gneiss\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 216px;\"\u003e\n \u003cp\u003eFluvio- marine deposits\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 216px;\"\u003e\n \u003cp\u003eMeta Gabbro\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 216px;\"\u003e\n \u003cp\u003eMeta- pellite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 216px;\"\u003e\n \u003cp\u003eGranite Gneiss\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 216px;\"\u003e\n \u003cp\u003eLaterite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 216px;\"\u003e\n \u003cp\u003eTTG Gneiss\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" rowspan=\"5\" style=\"width: 51px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" rowspan=\"5\" style=\"width: 108px;\"\u003e\n \u003cp\u003eGeomorphology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 216px;\"\u003e\n \u003cp\u003ePediment- Pediplain Complex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" rowspan=\"5\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" rowspan=\"5\" style=\"width: 103px;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 87px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 216px;\"\u003e\n \u003cp\u003eYounger Coastal Plain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 87px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 216px;\"\u003e\n \u003cp\u003eModerately Dissected Lower Plateau\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 87px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 216px;\"\u003e\n \u003cp\u003eModerately Dissected Hills and Valleys\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 87px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 216px;\"\u003e\n \u003cp\u003eModerately Dissected Lower Plateau\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 87px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" rowspan=\"4\" style=\"width: 51px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" rowspan=\"4\" style=\"width: 108px;\"\u003e\n \u003cp\u003eSlope\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 216px;\"\u003e\n \u003cp\u003e1. (0\u003csup\u003eo\u003c/sup\u003e- 4\u003csup\u003eo\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" rowspan=\"4\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" rowspan=\"4\" style=\"width: 103px;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 87px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 216px;\"\u003e\n \u003cp\u003e2. (4\u003csup\u003eo\u003c/sup\u003e-9\u003csup\u003eo\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 87px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 216px;\"\u003e\n \u003cp\u003e3. (9\u003csup\u003eo\u003c/sup\u003e-16\u003csup\u003eo\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 87px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 216px;\"\u003e\n \u003cp\u003e4. (16\u003csup\u003eo\u0026nbsp;\u003c/sup\u003e\u0026ndash; 41\u003csup\u003eo\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 87px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" rowspan=\"5\" style=\"width: 51px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" rowspan=\"5\" style=\"width: 108px;\"\u003e\n \u003cp\u003eDrainage Density\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 216px;\"\u003e\n \u003cp\u003e1. (0- 0.5 km/km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" rowspan=\"5\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" rowspan=\"5\" style=\"width: 103px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 216px;\"\u003e\n \u003cp\u003e2. (0.5 \u0026ndash; 1.5 km/km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 216px;\"\u003e\n \u003cp\u003e3. (1.5 \u0026ndash; 2.5km/km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 216px;\"\u003e\n \u003cp\u003e4. (2.5 \u0026ndash; 3.5 km/km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 216px;\"\u003e\n \u003cp\u003e5. (3.5 \u0026ndash; 6.5 km/km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" rowspan=\"4\" style=\"width: 51px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" rowspan=\"4\" style=\"width: 108px;\"\u003e\n \u003cp\u003eSoil Type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 216px;\"\u003e\n \u003cp\u003eClayey Soil\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" rowspan=\"4\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" rowspan=\"4\" style=\"width: 103px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 216px;\"\u003e\n \u003cp\u003eLoamy Soil- Undulating Slope\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 216px;\"\u003e\n \u003cp\u003eLoamy Soil- Foot hill\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 216px;\"\u003e\n \u003cp\u003eLoamy Soil- Hills\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" rowspan=\"5\" style=\"width: 51px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" rowspan=\"5\" style=\"width: 108px;\"\u003e\n \u003cp\u003eLineament Density\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 216px;\"\u003e\n \u003cp\u003e1. (0- 0.5 km/km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" rowspan=\"5\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" rowspan=\"5\" style=\"width: 103px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 87px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 216px;\"\u003e\n \u003cp\u003e2. (0.5 \u0026ndash; 1 km/km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 87px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 216px;\"\u003e\n \u003cp\u003e3. (1 \u0026ndash; 1.5km/km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 87px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 216px;\"\u003e\n \u003cp\u003e4. (1.5 \u0026ndash; 2 km/km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 87px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 216px;\"\u003e\n \u003cp\u003e5. (2 \u0026ndash; 2.6 km/km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 87px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" rowspan=\"7\" style=\"width: 51px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" rowspan=\"7\" style=\"width: 108px;\"\u003e\n \u003cp\u003eLULC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 216px;\"\u003e\n \u003cp\u003eEvergreen Broadleaf forest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" rowspan=\"7\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" rowspan=\"7\" style=\"width: 103px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 87px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 216px;\"\u003e\n \u003cp\u003eScrubland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 87px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 216px;\"\u003e\n \u003cp\u003eCropland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 87px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 216px;\"\u003e\n \u003cp\u003eWaterbody\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 87px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 216px;\"\u003e\n \u003cp\u003eMixed Forest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 87px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 216px;\"\u003e\n \u003cp\u003eDecidous Broadleaf forest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 87px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 216px;\"\u003e\n \u003cp\u003ePlantation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 87px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable. Areas wise suitability for the recharge zone\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"370\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCategories\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eArea (km\u003csup\u003e2\u003c/sup\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePercentage\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 151px;\"\u003e\n \u003cp\u003eUnsuitable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e18.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003e14.24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 151px;\"\u003e\n \u003cp\u003eModerately Suitable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e24.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003e19.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 151px;\"\u003e\n \u003cp\u003eHighly Suitable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e71.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003e55.94\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 151px;\"\u003e\n \u003cp\u003eVery Highly suitable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e13.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003e10.56\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe artificial groundwater recharge zone map presented in Figure 9 illustrates the spatial distribution of areas suitable for artificial recharge within the Mochemad River Basin. The northeastern and southwestern regions of the basin have been categorized as having low potential for artificial recharge, occupying approximately 14.24% of the total area. These areas encompass villages such as Malgaon, Nhaichiad, Mochemad, Ansur, and Tulas, where the predominant geological formations consist of Laterite and Granite Gneiss. These lithological units are often associated with steeply sloping terrain, which promotes rapid surface runoff and limits water infiltration. Furthermore, the presence of moderately to highly dissected hills, valleys, and lateritic plateaus exacerbates the problem, as the uneven and elevated topography inhibits groundwater percolation. These conditions make these regions less suitable for artificial recharge interventions.\u003c/p\u003e\n\u003cp\u003eIn contrast, a significant portion of the basin, particularly the central region, has been classified as Highly Suitable to Very Highly Suitable for artificial groundwater recharge. This area is characterized by gentle to moderate slopes, dense lineament networks, moderate drainage density, and permeable soil and geological formations, which collectively enhance the infiltration capacity and groundwater recharge potential. These favorable conditions suggest that the central part of the Mochemad River Basin holds considerable promise for implementing artificial recharge structures such as percolation tanks, check dams, continuous contour trenches (CCT), and recharge shafts. This zonation is crucial for guiding future groundwater management strategies in the region, ensuring that artificial recharge efforts are optimally located for maximum effectiveness.\u003c/p\u003e\n\u003cp\u003eIn this analysis, suitable site for constructing artificial recharge and groundwater conservation structures is identified. Various artificial recharge techniques like surface spreading and construction of check dams, percolation ponds/tanks, bench trenching, contour barriers and surface irrigation can be employed in the demarcated favourable zones. Such structures will lower the surface runoff and enhance the infiltration rate (Bhattacharya, 2010).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eValidation of Artificial Groundwater Recharge Zones\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe validation of the delineated artificial groundwater recharge zones was conducted using both field-based and statistical approaches to ensure the robustness and accuracy of the proposed AHP-GIS model. This dual validation strategy strengthens confidence in the model\u0026rsquo;s applicability for practical groundwater recharge planning.\u003c/p\u003e\n\u003cp\u003eIn the field-based validation, groundwater level measurements were obtained from 39 wells distributed throughout the Mochemad River Basin during the post-monsoon season of December 2021. These wells were spatially analyzed in relation to the four classified artificial recharge suitability zones: Unsuitable, Moderately Suitable, Highly Suitable, and Very Highly Suitable. Among the 39 wells, 33 were in areas classified as Highly Suitable for artificial recharge, validating the model\u0026apos;s prediction that these zones exhibit favourable recharge potential. The water table depth in these zones ranged from 0.38 meters below ground level (bgl) to 2.83 m bgl, indicating shallow groundwater levels suitable for artificial recharge structures. Three wells were found in Moderately Suitable zones with groundwater depths ranging between 3.35 m bgl to 3.96 m bgl, and three wells were in Unsuitable zones, where water levels exceeded 5 m bgl, indicating deeper and less favorable conditions for recharge interventions.\u003c/p\u003e\n\u003cp\u003eFurthermore, the spatial location of Adeli Dam, a surface water harvesting structure constructed across the Mochemad River, was compared against the generated recharge zone map. The dam was located within the Moderately Suitable recharge zone, which aligns with hydrological planning principles, as areas with moderate infiltration capacity are ideal for constructing surface water harvesting structures such as dams and reservoirs (Mahmoud et al., 2014). This practical alignment supports the functional validity of the recharge zoning model, demonstrating its utility in guiding both groundwater recharge and surface water storage infrastructure planning.\u003c/p\u003e\n\u003cp\u003eIn addition to field validation, statistical validation of the model\u0026rsquo;s predictive performance was carried out using Receiver Operating Characteristic (ROC) curve analysis. A total of 65 well locations were used for this analysis, which were split into training and testing datasets to enhance statistical reliability. Seventy percent of the wells (46 points) were used for training the model, while the remaining 30% (19 points) were employed for testing. The ROC curve was developed by plotting the True Positive Rate (sensitivity) against the False Positive Rate (1 - specificity). This analysis evaluates the ability of the recharge zone model to correctly classify zones that correspond with actual well data. The Area Under the Curve (AUC) was calculated using the following trapezoidal integration formula:\u003c/p\u003e\n\u003cp\u003e\u003cimg 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width=\"289\" height=\"61\"\u003e\n \u003cv:shapetype id=\"_x0000_t75\" coordsize=\"21600,21600\" o:spt=\"75\" o:preferrelative=\"t\" path=\"m@4@5l@4@11@9@11@9@5xe\" filled=\"f\" stroked=\"f\"\u003e\n \u003cv:stroke joinstyle=\"miter\"\u003e\n \u003cv:formulas\u003e\n \u003cv:f eqn=\"if lineDrawn pixelLineWidth 0\"\u003e\n \u003cv:f eqn=\"sum @0 1 0\"\u003e\n \u003cv:f eqn=\"sum 0 0 @1\"\u003e\n \u003cv:f eqn=\"prod @2 1 2\"\u003e\n \u003cv:f eqn=\"prod @3 21600 pixelWidth\"\u003e\n \u003cv:f eqn=\"prod @3 21600 pixelHeight\"\u003e\n \u003cv:f eqn=\"sum @0 0 1\"\u003e\u0026nbsp;\u003cv:f eqn=\"prod @6 1 2\"\u003e\u0026nbsp;\u003cv:f eqn=\"prod @7 21600 pixelWidth\"\u003e\u0026nbsp;\u003cv:f eqn=\"sum @8 21600 0\"\u003e\u0026nbsp;\u003cv:f eqn=\"prod @7 21600 pixelHeight\"\u003e\u0026nbsp;\u003cv:f eqn=\"sum @10 21600 0\"\u003e\u0026nbsp;\u003c/v:f\u003e\n \u003c/v:f\u003e\n \u003c/v:f\u003e\n \u003c/v:f\u003e\n \u003c/v:f\u003e\n \u003c/v:f\u003e\n \u003c/v:f\u003e\n \u003c/v:f\u003e\n \u003c/v:f\u003e\n \u003c/v:f\u003e\n \u003c/v:f\u003e\n \u003c/v:f\u003e\n \u003c/v:formulas\u003e\n \u003cv:path o:extrusionok=\"f\" gradientshapeok=\"t\" o:connecttype=\"rect\"\u003e\u0026nbsp;\u003c/v:path\u003e\n \u003c/v:stroke\u003e\n \u003c/v:shapetype\u003e\n \u003cv:shape id=\"_x0000_i1025\" type=\"#_x0000_t75\"\u003e\u0026nbsp;\u003cv:imagedata src=\"file:///C%3A/Users/pgs9865/AppData/Local/Temp/msohtmlclip1/01/clip_image001.png\" o:title=\"\" chromakey=\"white\"\u003e\u0026nbsp;\u003c/v:imagedata\u003e\n \u003c/v:shape\u003e,\n\u003c/p\u003e\n\u003cp\u003eWhere:\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003eX represents the cumulative area of each recharge zone class\u003c/li\u003e\n \u003cli\u003eY indicates the cumulative number of wells falling within each zone\u003c/li\u003e\n \u003cli\u003en is the total number of recharge zone categories (in this case, four)\u003c/li\u003e\n \u003cli\u003eX₁, X₂ and Y₁, Y₂ are successive coordinates used in the integration\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe AUC value for the Mochemad River Basin recharge zone model was calculated to be 0.86, indicating an 86% accuracy rate (Figure 10). According to standard ROC interpretation criteria, this result places the model in the \u0026quot;Very Good\u0026quot; predictive performance category (0.8\u0026ndash;0.9), confirming that the spatial outputs of the AHP-GIS model closely correspond with observed groundwater behavior. When compared to similar studies, the AUC value of 0.84 aligns closely with results from Agarwal \u0026amp; Garg (2015), who reported an AUC of 0.85 using a similar AHP-GIS framework; Ababulgu \u0026amp; Molla (2025) who reported 0.77 in Ethiopia; and Abdo et al. (2024) in Syria who achieved 0.877 using 74 validation wells. Kodihal \u0026amp; Akhtar (2024) also achieved 0.90 using an AHP-OWA hybrid in a future-scenario-based analysis for Jaipur.\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eThis statistically robust outcome, combined with the practical field validation, affirms that the methodology used is not only scientifically sound but also of practical relevance to water resource planners, engineers, and policymakers.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study presents a comprehensive assessment of artificial groundwater recharge zones (AGRZ) in the Mochemad River Basin, a coastal region of Maharashtra characterized by high monsoonal rainfall, lateritic terrain, and increasing vulnerability to saline water intrusion. By integrating Remote Sensing (RS), Geographic Information Systems (GIS), and the Analytical Hierarchy Process (AHP), the basin was classified into four recharge suitability zones\u0026mdash;unsuitable (14.24%), moderately suitable (19.25%), highly suitable (55.94%), and very highly suitable (10.56%).\u003c/p\u003e\n\u003cp\u003eThe key finding is that approximately 66.5% of the area falls under highly to very highly suitable zones for groundwater recharge. The use of multi-thematic layers such as lithology, slope, land use/land cover, drainage density, and lineament density provided a geospatially robust foundation for accurate classification. The ROC-AUC value of 84.6% confirms the reliability of the AHP-GIS model, further supported by field-based groundwater level data which correlated well with the spatial model outputs.\u003c/p\u003e\n\u003cp\u003eThe novelty of this research lies in its application of a decision-support AHP-GIS framework to a humid tropical coastal basin, a context seldom explored in recharge zone modeling. Most previous studies have concentrated on arid or semi-arid and basaltic regions. Here, the methodology addresses the unique challenges of lateritic soil, steep-slope erosion, and saline intrusion, offering a valuable precedent for similar coastal settings globally.\u003c/p\u003e\n\u003cp\u003eThe study also provides practical recommendations, proposing suitable recharge structures such as check dams, percolation tanks, Continuous Contour Trenches (CCT), and Mati Nala Bandharas (MNB), tailored to site-specific geomorphic and hydrological conditions. These outputs can directly aid water resource planners, engineers, and policymakers in designing and prioritizing interventions for groundwater conservation.\u003c/p\u003e\n\u003cp\u003eHowever, several limitations were identified. The model uses post-monsoon groundwater level data, which, although effective in indicating immediate recharge, may not capture the full seasonal variability. Additionally, the moderate resolution of input datasets and lack of long-term groundwater monitoring could impact the spatial precision of results. The study also assumes static land use and climate conditions, which may not hold in the face of rapid land development or climate change.\u003c/p\u003e\n\u003cp\u003eIn conclusion, this research provides a replicable, low-cost, and efficient spatial planning model for identifying groundwater recharge zones in coastal and lateritic regions. Future studies are recommended to incorporate multi-seasonal water level data, employ higher-resolution datasets, and integrate climate change projections to enhance temporal and spatial accuracy. This work lays a foundation for sustainable water management strategies in ecologically sensitive and data-scarce coastal basins.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contribution:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTejas S. Naik (Corresponding Author): Collects data, performs GIS-based analysis, conducts Analytical Hierarchy Process (AHP) modelling and statistical analysis.\u003c/p\u003e\n\u003cp\u003eSatyajit K. Gaikwad: Supervises the research, reviews the manuscript, and provides critical revisions and improvements.\u003c/p\u003e\n\u003cp\u003eSneha Sawant: Interprets hydrogeological parameters, prepares thematic maps and validates recharge potential zones.\u003c/p\u003e\n\u003cp\u003ePraveen N. Kamble: Assists in data processing, cartographic visualization, and manuscript drafting.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e: This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number:\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability:\u0026nbsp;\u003c/strong\u003eThe datasets generated and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Publish declaration\u003c/strong\u003e: Not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate declaration:\u003c/strong\u003e Not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u0026nbsp;\u003c/strong\u003eThe authors wish to thank all who assisted in conducting this work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest:\u0026nbsp;\u003c/strong\u003eThe authors declare no financial or non-financial interests exist that are directly or indirectly related to this work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declaration:\u0026nbsp;\u003c/strong\u003eThe authors confirm that all the research meets ethical guidelines.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eGithinji, T. 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GIS-based multi-criteria analysis for flood hazard mapping: A case study from the northwestern part of Egypt. Arabian Journal of Geosciences, 8(2), 953\u0026ndash;971. https://doi.org/10.1007/s12517-014-1257-2\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-geoscience","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Geoscience](https://www.springer.com/journal/44288)","snPcode":"44288","submissionUrl":"https://submission.nature.com/new-submission/44288","title":"Discover Geoscience","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Groundwater Recharge Modelling, Analytical Hierarchical Process (AHP), Geospatial Techniques, Coastal Aquifer Management, Climate Vulnerability","lastPublishedDoi":"10.21203/rs.3.rs-9140855/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9140855/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eGroundwater depletion in coastal regions has intensified due to increasing anthropogenic pressures, particularly from ecotourism and climate-induced variability. The Mochemad River Basin, situated along the western coast of Maharashtra, India, is experiencing critical groundwater stress, including severe seawater intrusion. Despite receiving over 3500 mm of annual rainfall, inadequate recharge infrastructure has limited the sustainable replenishment of aquifers. This study employs a geospatial modelling approach integrated with the Analytical Hierarchical Process (AHP) to delineate Artificial Groundwater Recharge Zones (AGRZ) in this climate-vulnerable coastal basin. Seven thematic layers\u0026mdash;lithology, geomorphology, land use/land cover, slope, drainage density, lineament density, and rainfall\u0026mdash;were processed and weighted using AHP to reflect their relative influence on recharge potential. A weighted overlay analysis within a GIS environment produced a recharge suitability map categorizing the basin into four zones: unsuitable (14.24%), moderately suitable (19.25%), highly suitable (55.94%), and very highly suitable (10.56%). Model validation using the Receiver Operating Characteristics (ROC) curve yielded an Area Under the Curve (AUC) of 0.89, indicating strong predictive performance. The study\u0026rsquo;s novelty lies in the integration of high-resolution spatial datasets with AHP in a high-rainfall, climate-sensitive coastal context. Based on the identified zones, suitable site-specific recharge interventions such as check dams, infiltration tanks, Mati Bandaras, and Continuous Contour Trenches (CCTs) are recommended to enhance groundwater resilience and sustainable water resource management in the region.\u003c/p\u003e","manuscriptTitle":"Geospatial Modelling and Multi-Criteria Evaluation of Groundwater Recharge Potential in a Climate-Stressed Coastal Basin of Western India","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-13 11:12:13","doi":"10.21203/rs.3.rs-9140855/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-17T16:06:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"138391187056971929985411270508809990391","date":"2026-05-17T15:05:25+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-28T12:11:01+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-21T01:57:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"32074897405684160762409154812224346380","date":"2026-04-13T05:50:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"181348337357905513931749492795072855067","date":"2026-04-09T06:42:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"331075841331592186890947780436427102253","date":"2026-04-09T05:16:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"69472816112105248181779756216841847698","date":"2026-04-09T03:45:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"207983309654573802123190321288893855336","date":"2026-04-08T04:49:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"119479110989200451606512844703043791182","date":"2026-04-07T10:34:20+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-07T05:08:07+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-01T18:15:24+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-30T11:59:50+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-25T18:35:51+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Geoscience","date":"2026-03-25T17:15:48+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-geoscience","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Geoscience](https://www.springer.com/journal/44288)","snPcode":"44288","submissionUrl":"https://submission.nature.com/new-submission/44288","title":"Discover Geoscience","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"56fa644b-2dc2-4385-b227-3a7f9210f1ec","owner":[],"postedDate":"April 13th, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-17T16:06:41+00:00","index":55,"fulltext":""},{"type":"reviewerAgreed","content":"138391187056971929985411270508809990391","date":"2026-05-17T15:05:25+00:00","index":54,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-13T11:12:13+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-13 11:12:13","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9140855","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9140855","identity":"rs-9140855","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}