Interpretable machine learning for groundwater-level prediction incorporating pumping-well power consumption

Abstract

Groundwater-level forecasting is essential for adaptive groundwater management in regions where pumping pressure and climatic variability interact, yet direct abstraction records are often unavailable. This study developed an interpretable extreme gradient boosting (XGB) framework, selected for its suitability for nonlinear tabular prediction under limited-data conditions, to predict monthly groundwater levels at nine monitoring wells in the mid-fan region of the Choushui River alluvial fan, Taiwan, using observations from January 2013 to June 2023. Rather than converting electricity use into pumping volume through a fixed and potentially uncertain coefficient, monthly pumping-well power consumption was used directly as an operational predictor of withdrawal-related stress. Three input configurations were evaluated to distinguish the predictive roles of hydro-meteorological forcing, local pumping-related stress, and cross-well groundwater-memory information. Out-of-sample testing revealed substantial between-well variability under the baseline setting (RMSE = 0.341–3.254 m; MAE = 0.247–2.783 m), with markedly larger errors in several pumping-sensitive wells. Adding pumping-well power consumption and cross-well lagged groundwater-level information produced directional reductions in average testing MAE, but these improvements were substantial in magnitude rather than statistically conclusive across wells under the present sample size. Specifically, average testing MAE decreased by 24.04% from Case A to Case B and by a further 9.27% from Case B to Case C, although one-sided Wilcoxon signed-rank tests on paired well-wise testing MAE values indicated that the differences between Cases A and B and between Cases B and C were not statistically significant at the across-well level. In contrast, relative to a one-step persistence benchmark, the corresponding skill scores were 6.23% for Case A, 28.79% for Case B, and 35.39% for Case C, and the final model significantly outperformed persistence. SHAP analysis showed that lag-1 groundwater level was the dominant predictor, while pumping-related power-consumption variables provided substantial additional explanatory information in pumping-sensitive wells. These results indicate that operational power-consumption data can be integrated into an interpretable machine-learning framework to improve monthly groundwater-level prediction beyond simple persistence in data-scarce aquifers, while also providing a practical basis for month-ahead groundwater-risk screening and future research on human-induced groundwater stress.