A novel multivariate approach for water quality index prediction irrespective of the geospatial distinction of water sources having varied end uses

Water quality assessment is fundamental to understanding the suitability of water for human consumption, ecological sustainability, and industrial applications. This study presents an empirical multi-parameter approach for estimating the Canadian Council of Ministers of the Environment (CCME)-based Water Quality Index (WQI) in Kota, Rajasthan, India. Eighteen samples from groundwater, surface water, and municipal supplies were analyzed for six parameters—pH, turbidity, total dissolved solids (TDS), hardness, alkalinity, and iron—following BIS IS 10500:2012 standards. Pareto analysis revealed turbidity and TDS as the most influential, independent drivers of WQI. GIS-based mapping captured spatial and seasonal variation, highlighting persistent water quality stress across the city. A quotient response function was developed using normalized parameters, and a predictive regression model was formulated with TDS and turbidity as core variables. The model achieved high accuracy, with R² values of 91.45% for groundwater, 96.05% for surface water, and 94.37% for municipal water. The results establish a scalable, source-independent framework for predicting WQI, offering robust support for urban water resource monitoring and sustainable management.