Remote Monitoring and Management of Water Quality in the Indian River Ganga: Regression Modelling Approach

Abstract There is a critical issue of water quality degradation in the River Ganga that threatens the health and welfare of millions of Indians. The River Ganga's water quality may be monitored and managed with the help of remote location water quality prediction. The goal of this work is to create and assess regression models and error analysis methods for predicting River Ganga water quality at remote locations. The data set used in the study comprises of water quality measurements taken at several sites throughout the River Ganga Basin. These measures include temperature, dissolved oxygen, pH, conductivity, biochemical oxygen demand, nitrate levels. The association between the independent factors (distance and temperature) and the dependent variables (water quality indicators) is established using a linear regression model. The water quality characteristics for unmonitored areas along the river are then predicted using the trained model. These findings enable preemptive actions to be made to reduce water pollution and guarantee the sustainability of the River Ganga ecosystem. They also shed light on the correlations between distance, temperature, and water quality indicators. The findings have consequences for human health, ecosystems, and economies reliant on the Ganga River, and they help build effective water quality monitoring and management measures.