Authors: Associate Professor P.Sandhya Krishna, Ala Nandini, Pavuluri Sri Lekha, Gumma Aparna, Patchava Pujitha
Abstract: Clean and safe drinking water is a crucial factor in the health of the population, but even now, delivery of contaminated drinking water remains one of the world issues. Water potability: a ML approach The use of ML models in Water Quality Assessment is a recent phenomenon in the past years, as it is now a highly promising tool that predicts the water potability in an efficient (more efficient than traditional) manner. The paper presents a smart machine learning system to anticipate the potability of water that is determined by undertaking a thorough review of diverse physico-chemical characteristics of water such as PH, Hardness, Solids, Chloramines, Sulfate and organic contaminants. State of art preprocessing methods are also applied to address missing values, outliers and feature stratification which enhance the quality and the strength of the data. There are several supervised learning processes, which include Random Forest, SVM, Gradient Boosting and ANN to determine the best predictive accuracy algorithm. The general performance is also justified with the premises of accuracy, precision, recall, F1-score and ROC-AUC performance parameters and demonstrates that the suggested framework implementation is reliable and efficient on actual water quality monitoring scenarios. Also, the work places emphasis on the effects of the feature selection and the hyperparmeter tuning on the enhancement of the prediction performance. Ensemble approach and cross-validation methods cut down on the framework and expand the generalization potential with different datasets.
