Hassan Rahoui, Ayoub Sguigaa, Mohamed Sabir, Said Chakiri, Imane Sebari, Allal Labriki, Mohamed Sadiki
Groundwater recharge in semi-arid mountainous environments is controlled by complex climatic, geological, topographical and anthropogenic interactions.In the Moroccan Atlas Mountains, snowmelt plays a crucial role in sustaining surface and groundwater resources, which are increasingly threatened by agricultural expansion and climate variability.In this context, the Midelt Aquifer within the Ansegmir River Watershed (ARW) represents a strategic water resource for irrigated agriculture, particularly apple orchards that heavily depend on groundwater extraction.This study aims to map the groundwater recharge potential of the Midelt aquifer (241 km²), in response to the expansion of orchards within the Oued Ansegmir watershed.The study integrates piezometric data, remote sensing (RS), GIS-based algorithms and machine learning (ML) approaches.Three modeling techniques were compared: frequency ratio (FR), Random Forest (RF) and extreme gradient boosting (XGBoost).The models were calibrated using 271 field measured piezometric observations used directly as the target variable (70:30), and 16 conditioning factors retained after a Pearson correlation and variance inflation factor (VIF) analyses.Performance was evaluated on an independent test set (n = 82) using ten complementary metrics, ROC curves, confusion matrices, radar plots, an inter-model agreement matrix, and pixel by pixel spatial consensus analysis.XGBoost outperformed the other two models (AUC = 0.769, F1 = 0.590, Kappa = 0.356) and demonstrated remarkable consistency in the spatial validation, particularly in identifying areas with low recharge potential.Therefore, it was selected to produce the final groundwater recharge potential map.These results provide a scientific basis for sustainable groundwater management and the prioritization of future drilling activities within the ARW.