Achieving Sustainable Water Solutions through Machine Learning Classification: Advancing Global Water Quality Goals

With the continuing rise in the contamination level of drinking supplies, water quality is becoming a major global concern. Thus, systems for regulating and categorizing water according to the presence of pollutants must be devised. In this research piece, Machine Learning (ML) Supervised Classification Techniques notably RandomForest, SVM and Naïve Bayes is used for the classification analysis. Feature selection approaches are applied further to boost the Efficiency and robustness of the classification model. The comparison of the accuracy of the classification models used is integrated using the ensemble approaches and the new classifier attained an accuracy of 97.80% which is greater as compared to the other classifiers employed. This classification is vital in making smart decisions about sustainable water resource management. This study plays a critical role in promoting Sustainable Development Goals (SDG) 6: Clean Water and Sanitation, by contributing to informed decision-making for sustainable water resource management. The established classification system can be essential in monitoring water quality, defending public health, and promoting sustainable practices in water resource management.