The spatial mapping and prediction of groundwater quality (GWQ) is important for sustainable groundwater management, but several research gaps remain unexplored, including the inaccuracy of spatial interpolation, limited consideration of the geological environment and human activity effects, limitation to specific pollutants, and unsystematic indicator selection. This study utilized the entropy-weighted water quality index (EWQI), the LightGBM model, the pressure-state-response (PSR) framework and SHapley Additive exPlanations (SHAP) analysis to address the above research gaps. The normalized importance (NI) shows that NO3− (0.208), Mg2+ (0.143), SO42− (0.110), Cr6+ (0.109) and Na+ (0.095) should be prioritized as parameters for remediation, and the skewness EWQI distribution indicates that although most sampled locations have acceptable GWQ, a few areas suffer from severely poor GWQ. The PSR framework identifies 13 indicators from geological environments and human activities for the SMP of GWQ. Despite high AUROCs (0.9074, 0.8981, 0.8885, 0.9043) across four random training and testing sets, it was surprising that significant spatial uncertainty was observed, with Pearson correlation coefficients (PCCs) from 0.5365 to 0.8066. We addressed this issue by using the spatial-grid average probabilities of four maps. Additionally, population and nighttime light are key indicators, while net recharge, land use and cover (LULC), and the degree of urbanization have the lowest importance. SHAP analysis highlights both positive and negative impacts of human activities on GWQ, identifying point-source pollution as the main cause of the poor GWQ in the study area. Due to the limited research on this field, future studies should focus on six key aspects: multi-method GWQ assessment, quantitative relationships between indicators and GWQ, comparisons of various spatial mapping and prediction models, the application of the PSR framework for indicator selection, the development of methods to reduce spatial uncertainty, and the use of explainable machine learning techniques in groundwater management.
