Data Mining to Identify Contaminant Event Locations in Water Distribution Systems
Publication: Journal of Water Resources Planning and Management
Volume 135, Issue 6
Abstract
To respond to growing concerns related to potential contamination ingress via backflow and/or terrorist threats to drinking water, a data mining approach is developed. Use of this data mining approach, in conjunction with a maximum likelihood procedure provides the means to identify the location and time of an intrusion event, based on limited sensor data. Uncertainties in water demand, sensor measurement, and modeling, are demonstrated to be highly relevant and necessary to be considered in the contamination identification problem. The effectiveness of the data mining method is demonstrated using a case study network where it takes only 3 min to identify a multiple injection event using five sensors in a 285 node water distribution network, including consideration of the aforementioned sources of uncertainty. The effectiveness of the method ensures the ability for a rapid-response to an abnormal event, and consequently, minimizes exposure risks of water consumers.
Get full access to this article
View all available purchase options and get full access to this article.
Acknowledgments
Financial support provided by both the UNSPECIFIEDCanada Research Chair Program and the Joint Infrastructure Interdependence Research Program are gratefully acknowledged. The comments and suggestions made by the reviewers have added substantially to the paper and are greatly appreciated.
References
Bahadur, R., Samuels, W. B., and Pickus, J. (2003). “Case study for a distribution system emergency response tool.” AWWA Research Foundation, Denver.
Babovic, V., Drécourt, J. P., Keijzer, M., and Hansen, P. F. (2002). “A data mining approach to modelling of water supply assets.” Urban Water, 4(4), 401–414.
CBC News. (2007). “CBC news in depth: Fighting crimes with databases.” ⟨http://www.cbc.ca/news/background/tech/data-mining.html⟩ (Feb. 29, 2008).
Cristo, C. D., and Leopardi, A. (2006). “Uncertainty effects on pollution source location in water networks.” The 8th Water Distribution System Analysis Symp., Cincinnati.
de Santics, A., Shang, F., and Uber, J. (2006). “Determining possible contaminant sources through flow path analysis.” The 8th Water Distribution System Analysis Symp., Cincinnati.
EPANET 2.0. Toolkits. (2000). ⟨www.epa.gov/ORD/NRMRL/wswrd/epanet.html⟩ (Feb. 24, 2008).
Ghazali, M., and McBean, E. (2009). “Current technologies for on-line monitoring of drinking water in distribution systems.” Conceptual modelling of urban water systems, monograph 17, CHI, Guelph, 381–395.
Guan, J., Aral, M. M., Maslia, M. L., and Grayman, W. M. (2006). “Identification of contaminant sources in water distribution systems using simulation-optimization method: Case study.” J. Water Resour. Plann. Manage., 132(4), 252–262.
Hill, J., Van Waanders, B., and Laird, C. (2006). “Source inversion with uncertain sensor measurements.” The 8th Water Distribution System Analysis Symp., Cincinnati.
Huang, J., and McBean, E. (2008). “Using bayesian statistics to estimate the chlorine wall decay coefficients for a water distribution system.” J. Water Resour. Plann. Manage., 134(2), 129–137.
Huang, J., McBean, E., and James, W. (2006). “Multi-objective optimization for monitoring sensor placement in water distribution systems.” The 8th Water Distribution System Analysis Symp., Cincinnati.
Huang, J., and McBean, E. A. (2007). “Water quality modeling using fault tree method.” Contemporary modeling of urban water systems, monograph 15, CHI, Guelph, 19.
Laird, C. D., and Biegler, L. T. (2006). “A mixed index approach for obtaining unique solutions in source inversion of drinking water networks.” The World Water & Environmental Resources Congress, EWRI, Anchorage, Alaska.
Laird, C. D., Biegler, L. T., Waanders, B. G. V. B., and Bartlett, R. A. (2005). “Contamination source determination for water networks.” J. Water Resour. Plann. Manage., 131(2), 125–134.
Palace, B. (1996). “Data mining: What is data mining?” ⟨http://www.anderson.ucla.edu/faculty/jason.frand/teacher/technologies/palace/datamining.htm⟩ (Feb 29, 2008).
Preis, A., and Ostfeld, A. (2006). “Contamination source identification in water systems: A hybrid model trees-linear programming scheme.” J. Water Resour. Plann. Manage., 132(4), 263–273.
Schuster, C., and McBean, E. (2008). ”Impacts of cathodic protection of pipe break probabilities: A Toronto case study.” Can. J. Civ. Eng., 35(2), 210–216.
Shang, F., Uber, J. G., and Polycarpou, M. M. (2002). “Particle back tracking algorithm for water distribution system analysis.” J. Environ. Eng., 128(5), 441–450.
Uber, J. (2005). “Identifiability of contaminant source characteristics in steady-state and time-varying network flows.” The World Water & Environmental Resources Congress, EWRI, Anchorage, Alaska.
Zhu, Z., and McBean, E. (2004). “Estimation of censored data water quality values using decomposable Markov networks.” Journal of Environmental Informatics, 4(2), 48–55.
Information & Authors
Information
Published In
Copyright
© 2009 ASCE.
History
Received: Sep 21, 2007
Accepted: Apr 7, 2009
Published online: Oct 15, 2009
Published in print: Nov 2009
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.