Integrated assessment of the Plio-quaternary sedimentary succession and groundwater mineralization forecasting in the Rharb Basin (Northwestern Morocco)

Jelbi, Mohammed; Mridekh, Abdelaziz; Taia, Soufiane; Kili, Malika; El Mansouri, Bouâbid; Magrane, Bouchaib

doi:10.1016/j.jafrearsci.2024.105277

Journal of African Earth Sciences

2024

DOI: 10.1016/j.jafrearsci.2024.105277

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Integrated assessment of the Plio-quaternary sedimentary succession and groundwater mineralization forecasting in the Rharb Basin (Northwestern Morocco)

Mohammed Jelbi,

Abdelaziz Mridekh,

Soufiane Taia

et al.

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

Elmotawakkil,

Enneya

2024

Journal of Hydroinformatics

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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.

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

Elmotawakkil,

Enneya

2024

Journal of Hydroinformatics

View full text Add to dashboard Cite

show abstract

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Integrated assessment of the Plio-quaternary sedimentary succession and groundwater mineralization forecasting in the Rharb Basin (Northwestern Morocco)

Cited by 1 publication

References 64 publications

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

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

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