Application of machine learning algorithms for groundwater level prediction in the Najafabad plain

Accurate groundwater level prediction is essential for sustainable water management in arid and semi-arid regions. This study evaluated three machine learning models-Extreme Gradient Boosting (XGBoost), Random Forest (RF), and Support Vector Machine (SVM)-to forecast groundwater levels across five hydrogeological zones of the Najafabad Plain, Iran. Input variables included climatic (precipitation, temperature), hydrological (previous groundwater level), and anthropogenic (irrigation and groundwater abstraction) factors. Model performance was assessed using the coefficient of determination (R²), root mean square error (RMSE), mean absolute error (MAE), Willmott's index (WI), and percent bias (PBIAS). Among the algorithms, XGBoost showed the best predictive skill, with mean testing results of R² = 0.8480, RMSE = 1.5540 m, MAE = 0.8800 m, WI = 0.9660, and PBIAS = + 0.0400%. The near-zero PBIAS, ranging from - 1.7000% to + 2.4000% across zones, indicates minimal bias and high robustness under heterogeneous hydrogeological conditions. In comparison, RF achieved moderate accuracy (R² = 0.7480), while SVM attained strong training performance (R² = 0.9180) but weaker generalization in testing (R² = 0.8220), reflecting overfitting. Overall, the results confirm the effectiveness of ensemble methods-particularly XGBoost-in groundwater prediction and highlight the importance of integrating climatic and anthropogenic drivers for sustainable aquifer management.