Interpretable Groundwater-Level Prediction in an Arid Inland Basin by Integrating Dempster–Shafer Feature Screening with a Stacking Ensemble

Abstract

Daily groundwater-level prediction in arid inland basins is driven by complex meteorological–hydrological conditions, water supply, pumping, and irrigation demand. Using data from the Zhangye Basin (2018–2025), this study selected 10 representative wells from 51 candidates to build a one-day-ahead framework with a 60-day input window. Dempster–Shafer evidence theory fused five criteria (Pearson, Spearman, lagged correlation, mutual information, and tree-model importance) to screen external variables. Long short-term memory network (LSTM), temporal convolutional network (TCN), and Transformer served as first-level sequence models; extreme gradient boosting (XGBoost) as the second-level stacking learner; and SHapley Additive exPlanations (SHAP) to quantify feature contributions. Dempster–Shafer evidence theory (D-S evidence theory) results indicated that groundwater pumping proxy variable (GPV), irrigation water-demand intensity proxy variable (IWD), surface-water supply proxy variable (SWS), canal-diversion proxy variable (CDV), air temperature (AT), runoff, vapor pressure deficit (VPD), and canal irrigation supply–demand coupling intensity (CISDCI) exhibited high process-representation relevance. During the 90-day test period, Stacking achieved the lowest RMSE for six of 10 wells. Regional average RMSE, MAE, and NSE values were 0.1596 m, 0.0772 m, and 0.9326 for the Zhangye group, and 0.0185 m, 0.0133 m, and 0.9177 for the Gaotai group. SHAP showed historical groundwater-level data dominated contributions, accounting for 64.17% and 43.96% in the Zhangye and Gaotai groups, respectively, and indicating model dependence rather than direct hydrological causality. This framework provides a cautious reference for short-term groundwater forecasting and input selection under the given data conditions.