Precision Mapping of Groundwater Potential in the Middle Seybouse Basin, Northeastern Algeria, via a Hybrid Geospatial and Predictive Modeling Approach

In semi-arid Mediterranean regions like the Middle Seybouse Watershed (Northeastern Algeria), the accurate spatial prediction of environmental groundwater potential is vital for sustainable resource management and risk mitigation. This study develops a high-resolution mapping framework by integrating multi-source geospatial data managed by a geographic information system (GIS) with optimized ensemble machine learning algorithms to identify the groundwater potential zones map. Initially, 18 environmental conditioning factors covering topographic, climatic, and hydrogeomorphologic elements were evaluated for data redundancy through dimensionality reduction techniques including Pearson correlation and multicollinearity diagnostics. The research identified a refined set of 10 independent predictors: elevation, land use/land cover (LULC), Topographic Wetness Index (TWI), Topographic Position Index (TPI), roughness, curvature, rainfall, drainage density, soil texture, and soil type, all maintaining a variance inflation factor (VIF) of less than 3.15 to ensure both model simplicity and stability. Four advanced machine learning algorithms, including random forest (RF), support vector machine (SVM), light gradient boosting machine (LGBM), and a stacking ensemble, were subsequently trained and validated using an 80/20 data split. Among these, the LGBM model emerged with a high predictive performance, achieving an area under the curve (AUC) score of 0.944, closely followed by the stacking ensemble with an AUC of 0.943. The comprehensive 30 m-resolution potential map, which is classified into five classes (very low, low, medium, high, and very high), offers a robust, data-driven resource for regional stakeholders. This mapping exercise not only provides a transparent and replicable methodology, but also effectively connects machine learning outputs with geomorphological insights in intricate hydrological settings, thus supporting sustainable environmental management and decision-making in the region.