Interpretable machine learning framework for managing shallow water table rise in urban aquifers

ABSTRACT This study presents the first attempt to develop interpretable machine learning (ML) models for simulating groundwater fluctuations in urbanized aquifers in rainfall-scarce regions. The ML-based modeling approach was designed to provide urban water managers with a reliable tool for controlling the development of shallow water tables resulting from artificial recharge. Support vector machine, Gaussian process regression, and regression tree models were constructed to simulate historical groundwater levels (GWLs) in four wells in Kuwait City. Groundwater data preprocessing was conducted to isolate the effects of artificial recharge activities and improve the performance of the ML models. The detrended GWLs were autocorrelated to determine the input delays for the ML models. The Local Interpretable Model-agnostic Explanation (LIME) technique and SHapley Additive exPlanations (SHAP) were utilized to interpret the models' outcomes. The R2 values for the wells examined in this study ranged from 0.75 to 0.98 during validation. The outcomes of the techniques employed revealed that the ML-based approach was superior to other frameworks, with a 50% decrease in the mean absolute error compared to statistical models. The findings of this study provide urban planners in arid regions with a useful strategy for managing shallow water tables.