Understanding the evolutionary processes and causes of groundwater drought using an interpretable machine learning model.

Drought is a widespread natural disaster, and rapid assessment of groundwater drought has become a challenge due to the lack of direct spatiotemporal observation of groundwater. We employed machine learning models and the Shapley Additive Explanation (SHAP), a game theory-based interpretability method, to understand and predict the evolution of groundwater drought by evaluating eight models with SHAP analysis in the West Liao River Plain (WLRP), with a semi-arid climate. The research showed: (1) The XGBoost model, optimized by the Sparrow Search Algorithm (SSA), achieved the highest performance (AUC: 0.