Computation of Water Quality Index and Its Estimation Using Machine Learning Techniques

Water quality is an essential measure for maintaining health and quality of life. In this study, the water quality index has been computed for Gautam Buddha Nagar using the Brown et al. method by collection of 51 groundwater samples. For this purpose, six physicochemical water parameters were analysed, namely pH, Hardness, Turbidity, C.O.D., D.O. and B.O.D. The readings indicate that the groundwater condition of Gautam Buddha Nagar is extremely poor. The computation of the Water Quality Index is a complex task. The study found that when all input variables are available, Machine Learning Techniques can be employed to vastly reduce the complexity in the computation while giving a high accuracy of 99.99% using Linear Regression, followed by an accuracy of 99.97% using Support Vector Regressor. Collection of input data is a time-consuming and costly process, therefore, the dimensionality of the input data was reduced through correlation analysis in an attempt to compute the water quality index by using just a single parameter. The best score of 81.05% was obtained using Linear Regression when Turbidity was used as the only feature, due to its high correlation with the target variable. The algorithms used for this analysis are: Linear Regression, Support Vector Regressor, Decision Tree and Random Forest Regressor.