Comparative assessment of machine learning models for seasonal groundwater-level prediction in the Hiran Basin, India

Abstract Groundwater monitoring and water-resource management in the context of growing climatic variability requires accurate prediction of groundwater levels. In this study, six machine-learning models were tested including Random Forest (RF), Extreme Gradient Boosting (XGB), Extra Trees Regressor (ETR), Histogram Gradient Boosting (HGB), Artificial Neural Network (ANN), and Support Vector Regression (SVR) models to predict seasonal groundwater levels in the Hiran Basin using hydroclimatic and antecedent groundwater-level variables. Precipitation, maximum temperature, minimum temperature, and four groundwater-level variables that were lagged by varying amounts were used in developing the models, and these variables were obtained from long-term observations of groundwater levels in monitoring wells throughout the basin. The performance of the models was evaluated by using a chronological validation framework and comparing the predictive ability of the six algorithms. The analysis revealed that the ANN model performed best in predicting overall results with HGB, XGB, RF, SVR, and ETR also giving reliable predictions. Precipitation and antecedent groundwater levels were the most important variables for explaining groundwater-level change in the SHAP analysis. The proposed framework is practical for seasonal groundwater-level forecasting and can be applicable for monitoring groundwater levels and managing groundwater resources in the Hiran Basin.