Exploring Machine Learning Algorithms for Reliable Water Quality Prediction

Abstract

Ensuring safe drinking water is a vital worldwide task. Accurate water quality prediction is crucial for protecting public health and the environment. Machine learning provides promising solutions for this objective. The study investigates the issue of precisely forecasting water quality with machine learning models. It examines different models and their efficacy in forecasting water quality features utilizing a given dataset. We conducted a comprehensive analysis of multiple machine learning models, including Bagging(REPTree), Multilayer Perceptron, M5P, Additive Regression, Stacking, Random Forest, and Decision Table. Firstly, ourselves imported the dataset into Weka, selected and configured the models, trained them on the dataset, and evaluated their performance using various metrics. Bagging (REPTree) outperformed compared to other models, showing its effectiveness in predicting water quality. Model selection depends on goals and constraints. Future research opportunities include feature engineering, ensemble methods, and data quality issues. The study concludes that Bagging (REPTree) classifier is a strong candidate for properly predicting water quality attributes. Future research should focus on improving feature engineering, exploring ensemble methods, expanding the dataset, and enhancing model explain ability. Deploying selected models for continuous monitoring and early detection can contribute to safer water supplies and sustainable water management practices. Compliance with water quality regulations can be better ensured through the application of these models. Overall, this study offers valuable insights regarding the application of machine learning for water quality prediction and highlights future directions for research and application in this important area.