Using data mining techniques to isolate chemical intrusion in water distribution systems

• Published in: Environmental monitoring and assessment Vol. 194; no. 3; p. 203
• Main Authors: Barros, Daniel Bezerra, Cardoso, Sandra Maria, Oliveira, Eva, Brentan, Bruno, Ribeiro, Lubienska
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.03.2022; Springer Nature B.V
• Subjects: Algorithms; Atmospheric Protection/Air Quality Control/Air Pollution; Chlorine; computer software; Contamination; Data analysis; Data Mining; Data processing; Distribution; Earth and Environmental Science; Ecology; Ecotoxicology; Environment; Environmental Management; Environmental monitoring; Environmental Monitoring - methods; Environmental science; Hydraulics; K-nearest neighbors algorithm; Learning algorithms; Linear programming; Localization; Machine learning; Monitoring systems; Monitoring/Environmental Analysis; Pesticides; Public health; Security; Sensors; Simulation; Software; Water; Water distribution; Water distribution systems; Water engineering; Water pollution; Water Quality; Water Supply; Water utilities; Water-supply; Random forest algorithm; Contamination sources location; K-nearest neighbor algorithm
• ISSN: 0167-6369; 1573-2959; 1573-2959
• DOI: 10.1007/s10661-022-09867-z

The security of water distribution systems has become the subject of an increasing volume of research over the last decade. Data analysis and machine learning are linked to hydraulic and quality modeling for improving the capacity of water utilities to save lives when faced with the contamination of water networks. This research applies k-nearest neighbor and random forest algorithms to estimate the location of contamination sources at near-real time. Epanet and Epanet-MSX software are used to simulate intrusions of pesticide into water distribution system and the interaction with compounds already present in water bulk. Different pesticide concentrations are considered in the simulations, and chlorine monitoring occurs through placed quality sensors. The results show that random forest can localize 88 % of contamination scenarios, while the KNN algorithm found 87 % . Finally, an assessment of contamination spread is made for a better understanding of the impacts of non-localized contamination.