Enhancing groundwater potential zone mapping with a hybrid analytical method: The case of semiarid basin

Groundwater resource management is a critical component of sustainable water use, necessitating accurate and nuanced mapping of groundwater potential zones. This study analyzed the groundwater potential of Algeria's 43,750 km2 Chelif Basin (more than 17% of the area of Northern Algeria) using a combination of both subjective and objective mapping techniques. The adopted approaches encompass the benchmark analytical Fuzzy Analytic Hierarchy Process (Fuzzy-AHP) and the DEcision MAking Trial and Evaluation Laboratory (DEMATEL) methodologies to quantify interdependencies of criteria related to groundwater potential. The analysis focused on ten criteria related to groundwater potential, including core moisture availability and key hydrological factors like distance to river, topographic wetness index, and hydrological soils. Fuzzy-AHP achieved slightly higher groundwater prospecting accuracy (AUC = 0.730) than classic AHP (0.716), with benchmarking against 15 related studies indicating robust performance. Instead of the most commonly used criteria in groundwater literature such as lineament, stream order, recharge rate, and drainage density, this study employed alternative factors to challenge and validate the efficacy of the proposed methodology. The decision to omit certain criteria facilitated a more focused and manageable analysis, yet still delivered a robust evaluation of groundwater potential in the studied area. Moreover, this approach underscores the adaptability of the proposed methodology to accommodate varying sets of criteria, tailored according to the availability of data and specific research objectives. Additionally, the DEMATEL evaluation reveals new insights into subtle prioritization divergences, specifically between domain specialist opinions and analytical assessments of the criteria. The integration of fuzzy logic and causal relationship mapping through DEMATEL provides a comprehensive and robust foundation for groundwater potential modeling. © 2024 Elsevier B.V.