This paper explores two applied classification models alerting for contamination events in water distribution systems. The models perform multivariate analysis of water quality online measurements for event detection. The developed models comprise an outlier detection algorithm and a following sequence analysis for the classification of events. The first model is an unsupervised minimum volume ellipsoid (MVE), which utilizes only normal operation measurements but requires calibration. The second is a supervised weighted support vector machine, which utilizes event examples and performs data-driven optimized calibration. The models were trained and tested on real water utility data with randomly simulated events that were superimposed on the original database. The models showed high accuracy and detection ability compared to previous studies. All in all, the MVE model achieved preferable results.
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August 2015
This article was originally published in
Journal of Water Supply: Research and Technology-Aqua
Article Contents
Research Article|
July 24 2014
Comparison of two multivariate classification models for contamination event detection in water quality time series
Nurit Oliker;
Nurit Oliker
1Faculty of Civil and Environmental Engineering, Technion – Israel Institute of Technology, Haifa 32000, Israel
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Avi Ostfeld
1Faculty of Civil and Environmental Engineering, Technion – Israel Institute of Technology, Haifa 32000, Israel
E-mail: [email protected]
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Journal of Water Supply: Research and Technology-Aqua (2015) 64 (5): 558–566.
Article history
Received:
March 20 2014
Accepted:
June 05 2014
Citation
Nurit Oliker, Avi Ostfeld; Comparison of two multivariate classification models for contamination event detection in water quality time series. Journal of Water Supply: Research and Technology-Aqua 1 August 2015; 64 (5): 558–566. doi: https://doi.org/10.2166/aqua.2014.033
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