Estimation and Prediction of Hydrological Variables Using Machine Learning Algorithms for Groundwater Management: ErfoudRadier Station in Morocco

Rachid El Ansari (), Ahmed Regragui (), Mohamed El Bouhaddioui (), Jamal Elhassan (), Youssef Rissouni (), Hicham Boutracheh (), Moulay Othman Aboutafail () and Aniss Moumen ()
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Rachid El Ansari: National School of Applied Sciences, Ibn Tofaïl University, Laboratory of Engineering Sciences
Ahmed Regragui: Moulay Ismail University of Meknes, Department of Computer Science
Mohamed El Bouhaddioui: National School of Mines Rabat
Jamal Elhassan: National School of Applied Sciences, Ibn Tofaïl University, Laboratory of Engineering Sciences
Youssef Rissouni: National School of Applied Sciences, Ibn Tofaïl University, Laboratory of Engineering Sciences
Hicham Boutracheh: National School of Applied Sciences, Ibn Tofaïl University, Laboratory of Engineering Sciences
Moulay Othman Aboutafail: National School of Applied Sciences, Ibn Tofaïl University, Laboratory of Engineering Sciences
Aniss Moumen: National School of Applied Sciences, Ibn Tofaïl University, Laboratory of Engineering Sciences

A chapter in Generative AI and Optimization Techniques for Sustainable Water Management, 2026, pp 263-280 from Springer

Abstract: Abstract Accurate prediction and estimation of missing values for hydrological and meteorological variables are crucial for the sustainable management of water resources in arid and semi-arid environments, particularly in Morocco. This work aims to evaluate the effectiveness of several machine learning algorithms for the prediction and estimation of missing mean temperature values at the ErfoudRadier station in the GZR basin. Five Machine Learning algorithms were applied and compared: Artificial Neural Network (ANN), Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), and K-Nearest Neighbors (KNN). Performance was evaluated on test data using evaluation metrics including Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and the Coefficient of Determination (R2). The comparative analysis highlights the superiority of the ANN and SVM models. The ANN model exhibits the lowest errors (RMSE = 2.954; MAE = 1.863) and a coefficient of determination nearly similar to that of the SVM (R2 = 0.878), while the SVM achieves the highest coefficient of determination with R2 = 0.8822. The RF (R2 = 0.820) and KNN (R2 = 0.821) models show more modest performance, and the Decision Tree (DT) produces the highest errors (RMSE = 4.765), confirming its limited suitability for modeling this type of continuous data. The results confirm the effectiveness of machine learning models, particularly ANN and SVM, for modeling hydrological and meteorological variables at the Erfoud station, significantly improving the reliability of forecasts and enabling more informed water management.

Keywords: Machine learning; Artificial Neural Network (ANN); Support vector machine (SVM); Hydrological modeling; Meteorological variables water management (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:spr:spochp:978-3-032-19012-3_15

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DOI: 10.1007/978-3-032-19012-3_15

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