Comparative Evaluation of Multi-Criteria Decision Analysis Techniques for Groundwater Potential Mapping in a Coastal Alluvial Basin of Eastern India

Groundwater potential zoning (GWPZ) using multi-criteria decision analysis (MCDA) and data-driven techniques has emerged as a vital tool for sustainable groundwater management, particularly in data-scarce coastal regions where surface water availability is limited and aquifers are vulnerable to overexploitation and seawater intrusion. Coastal aquifer systems are inherently heterogeneous and dynamically influenced by geomorphology, lithology, land use, and climatic conditions, making integrated decision-support frameworks essential for identifying zones with varying groundwater potential. In this context, the present study evaluates and compares six widely used subjective, objective and probabilistic MCDA techniques—Analytic Hierarchy Process (AHP), Fuzzy-AHP (F-AHP), Frequency Ratio (FR), Weight of Evidence (WoE), Entropy, and Multi-Influencing Factor (MIF), for analyzing groundwater prospect in a complex coastal alluvial setting. The study area with an areal extent of 6468.60 km², comprises the Haldi–Kansabati–Subarnarekha interfluve located along the Bay of Bengal in the southern coastal region of West Bengal, eastern India. The region is characterized by a ‘Tropical Wet-and-Dry’ climate under the Köppen–Geiger classification and exhibits pronounced hydrogeological heterogeneity. The geological framework is dominated by younger alluvium (quaternary sediments) in the floodplains and deltaic tracts, followed by older alluvium (tertiary sediments) inland, coastal alluvium along the shoreline, and lateritic formations in the landward uplands. These formations exert strong control on groundwater occurrence, storage, and movement. Multiple groundwater-conditioning factors such as: ‘runoff coefficient’, ‘land slope %’, ‘drainage density’, ‘geology’, and ‘proximity to surface water bodies’ representing topography, hydrology, geology, land surface characteristics, and recharge conditions were integrated within the ArcGIS pro v3.4.2 environment to generate GWPZ maps using each of the six MCDA techniques. The resulting groundwater potential maps were classified into three categories—‘high’, ‘moderate’, and ‘low’ potential zones—using consistent classification criteria across all methods to enable inter-model comparison. Model validation was performed using observed pumping well yield (discharge) data, which were independently categorized into three classes: ‘low’ (<36 m³/hr), ‘moderate’ (36–90 m³/hr), and ‘high’ (>90 m³/hr). The predictive performance of each GWPZ model was quantitatively evaluated using Pearson’s correlation coefficient (r), and Receiver Operating Characteristic (ROC) curve-derived Area Under the Curve (AUC) statistics generated through the ArcSDM v5.00.22 toolbox in ArcGIS Pro v3.4.2. The validation results demonstrate notable variability in model performance. Among the six MCDA techniques, Fuzzy-AHP (F-AHP) exhibited the highest predictive accuracy with a correlation coefficient (r) of 0.912 and an AUC value of 0.853, followed by AHP (r=0.897; AUC=0.815), WoE (r=0.868; AUC=0.773), FR (r=0.809; AUC=0.724), MIF (r=0.778; AUC=0.702), and Entropy (r=0.727; AUC=0.683). The superior performance of the hybrid Fuzzy-AHP technique highlights its robustness in handling uncertainty and subjectivity while maintaining logical consistency. The F-AHP technique further sustains gradual transitions in factor importance when dealing with complex coastal hydrogeological systems. Overall, the study underscores the robustness of MCDA-based approaches, particularly F-AHP and AHP, for groundwater potential assessment in coastal alluvial environments and provides a comparative framework to support groundwater planning and management in similar vulnerable coastal regions.