Predicting Groundwater Quality Using Machine Learning Models: A Case Study in Semnan Province, Iran

Groundwater in arid and semi-arid regions faces increasing pressures from over exploitation, climate variability, and declining quality, making accurate forecasting essential for sustainable management. This study examines two decades of hydrological and chemical data from Semnan Province, Iran (۲۰۰۳-۲۰۲۳), including precipitation, piezometric levels, and key water quality parameters. Eight nonlinear machine learning models, covering tree-based, ensemble, kernel-based, instance-based, and neural networks, were tested. Temporal patterns were captured using a walk-forward validation combined with exponentially weighted averaging. Ensemble methods, especially Random Forest and XGBoost, delivered the best performance, with an average R² of ۰.۹۰, RMSE of ۸۲۰.۰, and RMAE of ۲۱. These results show that combining different environmental data with advanced nonlinear models provides a reliable and flexible way to forecast groundwater quality in water-scarce areas.

کلیدواژه ها:

Machine Learning ، Groundwater ، Water Quality ، Walk-forward Validation