Analyzing the Water Quality Using Machine Learning Techniques

This study addresses the pressing issue of water quality deterioration in rivers, a consequence of urbanization, industrial expansion, and inadequate waste management. Leveraging machine learning, our research introduces an efficient and cost-effective method for water quality prediction and classification. Utilizing ridge, linear, and lasso regression models, we forecast the Water Quality Index (WQI), crucial for assessing water purity. Additionally, a range of machine learning classification models, including logistic regression, support vector machine, XGBoost, k-nearest neighbor, decision trees, random forest, and CATBoost, are employed for water quality and Designated Best Use (DBU) class classification. The results highlight the superiority of linear regression in WQI prediction, while XGBoost and CATBoost achieve perfect accuracy (100%) in water quality classification. Notably, CATBoost attains an 82% accuracy in DBU class classification. Derived from real-time data from Tamil Nadu, our methodology demonstrates the potential of machine learning in accurate water quality assessment, offering a valuable contribution to environmental sustainability and public health. In a world grappling with water scarcity and contamination, our approach stands as a promising tool for proactive water resource management and quality preservation.