Prediction Of Water Quality Using Effective Machine Learning Techniques

Abstract

One of the most vital natural resources for all earth's living things is water. Life's fundamental need is access to clean water. Water quality has substantially declined over the previous few decades as a result of pollution and numerous other problems. In this study, machine learning (ML) algorithms are developed to predict water quality and water quality classification (WQC). For the prediction of water quality classification, six machine learning algorithms Naïve Bayes, Random Forest (RF), Gradient Boosting (GBoost), K-nearest neighbor (K-NN),Logistic Regression (LogR), and Decision Tree (DT), have been used. The models were evaluated based on 16 parameters. The machine learning model’s result demonstrates the Random Forest model out performed than the other models.