This study presents an AI-driven framework for predicting groundwater storage (GWS) in the arid to semi-arid regions of Agdz and Zagora in southern Morocco, where sustainable water resource management is increasingly critical. Four machine-learning models Random Forest (RF), CatBoost, AdaBoost, and Multi-Layer Perceptron (MLP) were trained using a comprehensive dataset integrating Gravity Recovery and Climate Experiment (GRACE) mission-derived Terrestrial Water Storage (TWS), remote sensing indicators such as The Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST), and key climatic variables. To improve predictive accuracy, model hyperparameters were optimized using the Swan Optimization Algorithm (SOA), a bio-inspired metaheuristic technique. Among the tested models, RF achieved the highest performance, with root mean square error (RMSE) values of 4.70 mm and 4.29 mm and NSE scores of 0.998 and 0.999 for Agdz and Zagora, respectively. TWS consistently emerged as the most influential predictor across all models. These results highlight the potential of integrating artificial intelligence, satellite remote sensing, and bio-inspired optimization for periodically updated monitoring and prediction of groundwater storage in data-scarce regions. The proposed framework provides a valuable decision-support tool for smart irrigation planning and climate-resilient water management in agriculture-dependent areas.
Elmotawakkil et al. (Wed,) studied this question.