Advanced machine learning for predicting groundwater decline and drought in the Rabat–Salé–Kénitra region, Morocco

Abstract

The Rabat–Salé–Kénitra region of Morocco faces critical groundwater challenges due to increasing demands from population growth, agricultural expansion, and the impacts of prolonged droughts and climate change. This study employs advanced machine learning models, including artificial neural networks (ANN), gradient boosting (GB), support vector regression (SVR), decision tree (DT), and random forest (RF), to predict groundwater storage variations. The dataset encompasses hydrological, meteorological, and geological factors. Among the models evaluated, RF demonstrated superior performance, achieving a mean squared error (MSE) of 484.800, a root mean squared error (RMSE) of 22.018, a mean absolute error (MAE) of 14.986, and a coefficient of determination (R2) of 0.981. Sensitivity analysis revealed significant insights into how different models respond to variations in key environmental factors such as evapotranspiration and precipitation. Prophet was also integrated for its ability to handle seasonality in time-series data, further enhancing prediction reliability. The findings emphasize the urgent need to integrate advanced predictive models into groundwater management to address groundwater depletion and ensure sustainable water resources amid rising drought conditions. Policymakers can use these models to regulate extraction, promote water-saving technologies, and enhance recharge efforts, ensuring the sustainability of vital groundwater resources for future generations.