Study region The Middle Benue Trough, Nasarawa State, Nigeria. Study focus Groundwater quality deterioration is an increasing environmental and public health concern in this region; however, studies that integrate entropy-based water quality assessment, interpretable machine-learning modelling, explainable artificial intelligence, and health risk evaluation within a unified framework are limited. This study integrates hydrogeochemical analysis, entropy-weighted water quality indexing (EWQI), interpretable stacked-ensemble machine learning, and health risk assessment to evaluate seasonal groundwater quality dynamics across five Local Government Areas (LGAs). Twenty-four parameters from 600 groundwater samples collected during dry and rainy seasons were analysed. Hydrochemical facies comprised Na–Cl, mixed Ca-Mg-Cl, and Ca-Mg-HCO 3 water types, reflecting the combined influence of meteoritic recharge, carbonate and silicate weathering, cation exchange, and localised anthropogenic inputs. EWQI results identified Awe and Doma as contamination hotspots, with groundwater quality generally deteriorating during the rainy season and requiring management interventions. New hydrological insights for the region The stacked-ensemble model outperformed individual machine-learning algorithms for EWQI regression and classification, achieving excellent predictive performance (R 2 = 0.935; RMSE = 9.420) and classification accuracy (0.858). SHAP interpretation identifies As, Pb, Cd, and Mn as the main drivers of groundwater deterioration. Positive SHAP–HQ relationships demonstrated that contaminants exerting the greatest influence on groundwater quality deteriorations also contributed weak-to-moderate influence to health risks. The integrated framework provides a valuable tool to identify key contaminants, sensitive locations, and priority intervention targets in hydrogeologically complex areas.
Ogbeh et al. (Thu,) studied this question.