Performance Evaluation of Machine Learning Methods for Drinking Water Contamination Detection
Engineering Proceedings 69 2024
Valts Urbanovičs, Sergejs Paršutins, Jānis Rubulis, Mārtiņš Bonders, Katrīna Dambeniece, Roberts Ozols, Dāvids Štēbelis, Sandis Dejus

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.


Keywords
contamination; drinking water; machine learning; neural networks
DOI
10.3390/engproc2024069110
Hyperlink
https://www.mdpi.com/2673-4591/69/1/110

Urbanovičs, V., Paršutins, S., Rubulis, J., Bonders, M., Dambeniece, K., Ozols, R., Štēbelis, D., Dejus, S. Performance Evaluation of Machine Learning Methods for Drinking Water Contamination Detection. Engineering Proceedings 69, 2024, Vol. 69, No. 1, Article number 110. e-ISSN 2673-4591. Pieejams: doi:10.3390/engproc2024069110

Publication language
English (en)
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