Application of Machine Learning Algorithms for Groundwater Level Prediction in the Najafabad Plain

Abstract Accurate groundwater level prediction is vital for sustainable water management, particularly in arid and semi-arid regions under climatic and human-induced stress. This study investigates the performance of three machine learning algorithms—Extreme Gradient Boosting (XGBoost), Random Forest (RF), and Support Vector Machine (SVM)—to forecast groundwater levels in five hydrogeological zones of the Najafabad plain, Iran. Models were trained using climatic (precipitation, temperature), hydrological (previous groundwater level), and anthropogenic (irrigation, pumping) inputs. Performance was evaluated using R², RMSE, and MAE during training and testing phases. XGBoost outperformed others with a mean testing R² of 0.848 and RMSE as low as 1.554, showing high robustness, especially in zones with stable to moderately varying water tables. RF achieved moderate accuracy (mean R² = 0.748) with consistent error margins. SVM showed high training performance (R² = 0.918) but reduced generalization (testing R² = 0.822), indicating overfitting tendencies. Results highlight the effectiveness of ensemble models like XGBoost for heterogeneous aquifer systems. However, limited resolution of anthropogenic data remains a challenge. This study reinforces the value of machine learning—particularly gradient boosting—as a reliable, scalable approach for groundwater monitoring in data-scarce, complex environments.