Performance Evaluation of Machine Learning Methods for Drinking Water Contamination Detection

: The aim of the study is to train a machine learning (ML) model for drinking water contamination detection and compare performance to statistical methods and existing anomaly detection solutions. A pilot drinking water supply system was made and equipped with drinking water quality sensors and a contamination dosing system. The results from this study demonstrated that using the statistical Mahalanobis distance (MD) method to predict the classification of drinking water measurements yields a 99% accuracy, 23% precision, and 28% F-score result (for wastewater contamination); however, the ML model yields a 99% accuracy, 98% precision, and a 98% F-score result. The results show that the application of ML methods can improve drinking water contamination detection speed and accuracy.