Authors: Nitin Mishra
Abstract: Groundwater is the principal source of drinking and irrigation water in the Indo-Gangetic alluvial plains of Uttar Pradesh, India. Rapid urbanization, agricultural intensification, excessive groundwater abstraction, and geogenic contamination have significantly affected groundwater quality in the region. The present study evaluates groundwater quality in Central Uttar Pradesh using hydrogeochemical assessment, entropy-weighted water quality index (EWQI), and machine learning (ML) prediction techniques. A total of 178 groundwater samples were analyzed for major physicochemical parameters including pH, EC, TDS, TH, Ca2+, Mg2+, Na+, K+, HCO3−, Cl−, SO42−, NO3−, F−, SiO2, and CO32−. The entropy weight method was employed to minimize subjectivity in water quality assessment, while hydrogeochemical interpretations were carried out using Piper and Gibbs diagrams. Three machine learning models, namely Classification and Regression Tree (CART), Random Forest (RF), and Extreme Gradient Boosting (XGBoost), were implemented to predict groundwater quality conditions. The results revealed that groundwater chemistry is predominantly controlled by rock–water interaction and ion exchange processes, with Ca–HCO3 and mixed hydrochemical facies dominating the study area. The EWQI values indicated that most groundwater samples fall within good to medium drinking water quality categories, although localized fluoride enrichment was observed in several locations. Among the applied models, XGBoost demonstrated superior predictive capability with R2 = 0.9597, RMSE = 2.2376, and MAE = 1.7690, outperforming RF and CART models. The findings highlight the effectiveness of integrating GIS-based hydrogeochemical analysis with machine learning approaches for groundwater quality prediction and sustainable groundwater management in Central Uttar Pradesh.
DOI: http://doi.org/10.5281/zenodo.20268302
