Wederni, Khyria ， Haddaji, Boulbaba ， Hamed, Younes ， Bouri, Salem ， Colombani, Nicolo
2024-11-01 GROUNDWATER FOR SUSTAINABLE DEVELOPMENT 2024 27(卷), null(期), (null页)
Effective groundwater management is critical in arid and semi-arid regions, where water resources are essential for agriculture. This study assesses the Irrigation Water Quality Index (IWQI) of the Southern Gabe`s aquifer in Tunisia using a combination of traditional hydrochemical analysis and machine learning models-specifically, Classification and Regression Tree (CART) and Support Vector Machine (SVM). A total of 83 groundwater samples were analyzed based on five key parameters: Electrical Conductivity (EC), Sodium Adsorption Ratio (SAR), Chloride (Cl-), Sodium (Na+), and Bicarbonate (HCO3-). The results show that the CART model demonstrated superior performance with an R2 value of 0.99 and a Root Mean Square Error (RMSE) of 0.43, while the SVM model achieved an R2 of 0.87. These findings underscore CART's robustness in predicting IWQI, offering high precision even with limited datasets.