Vol. 7, Issue 6, Part B (2025)

Clean water is essential for life, health, and the environment. It plays a critical role in human survival, economic development, and ecological balance. In this study, multiple linear regressions are used for water quality index. The Water Quality Index (WQI) serves as a comprehensive measure to evaluate the overall quality of water based on various physicochemical parameters. This study employs multiple regression analysis to model the relationship between WQI and key water quality indicators such as pH, EC, TH, Ca, Na and other paramenters. The objective is to develop a predictive model that quantifies the impact of these independent variables on WQI and identifies the most significant contributors to water quality degradation. The Multiple Linear Regression Water Quality Index (MLRWQI) is fastest tool used for detection of water quality using mathematical expression so accuracy of this model is increased. In this model 12 parameters are considered for water quality index calculation. Determined regression value is considered with correlation values of each and every variable. The result of MLRWQI, residual standard error is 6.333, Multiple R² is 1, adjusted R² is 1 and p-value is 2.2e-16 received.