A GIS-based approach integrating fuzzy logic and AHP for groundwater recharge potential mapping in the Mostaganem plateau (Algeria)

In arid and semi-arid environments, groundwater constitutes an indispensable resource underpinning domestic water supply, agricultural production, and industrial activities. Faced with mounting anthropogenic and climatic pressures on water systems, characterizing and spatially delimiting zones favourable to aquifer replenishment has become a prerequisite for sound and sustainable water resource governance. The present work introduces a multi-criteria spatial framework aimed at producing a groundwater recharge potential distribution map. The approach integrates several infiltration-controlling factors through a Geographic Information System (GIS)-based environment, enhanced by fuzzy logic. Thematic datasets, topographic information, and in-situ measurements were processed and converted into geospatial layers. The relative weight of each controlling factor was established through the Analytic Hierarchy Process (AHP), while linear fuzzy membership functions were employed to transform each parameter into continuous membership values ranging from 0 to 1 according to its contribution to groundwater recharge. The resulting weighted layers were then combined to derive a spatially explicit representation of recharge potential across the study area. The proposed methodology was implemented over the Mostaganem plateau aquifer, a semi-arid mediterranean system in northwestern Algeria. The results produced a detailed map showing varied recharge potential across the region. The most favourable zones for recharge are predominantly identified in the northeastern and western sectors of the plateau, where permeable sandy deposits, calcareous sandstone formations, low-gradient terrain, and well-developed vegetation cover collectively promote water infiltration. Validation using well yield data, including cross-analysis and ROC evaluation, shows strong agreement with the recharge map, supporting the effectiveness of the fuzzy logic–GIS approach for groundwater assessment and sustainable management.