Real-Time Identification of Possible Contamination Sources Using Network Backtracking Methods
Publication: Journal of Water Resources Planning and Management
Volume 136, Issue 4
Abstract
In case of contamination intrusion in water distribution systems, water quality sensor data can be used to determine the location and time of the contamination source. One approach to contamination source identification is finding the source location that minimizes the difference between modeled and measured water quality. However, this is an inherently ill-posed mathematical problem, due to the shortage of measurements compared to source parameters, and regularization methods are required to force identification of a unique solution. An alternative practical method is developed in this paper to identify all possible locations and times that explain contamination incidents detected by the water quality sensors. Since sensors cannot detect the quantitative concentration of a contaminant, this method only requires a binary sensor status over time. A particle backtracking algorithm is used to identify the water flow paths and travel times leading to each sensor measurement. Those locations and times that are connected to positive sensor measurements, but are not connected to negative measurements, are the possible sources, assuming no false positive/negative readings and an accurate hydraulic model. The method also forms the basis for incorporating important concerns about hydraulic and sensor uncertainty, which are likely to enlarge the set of possible sources.
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Acknowledgments
This work was completed as part of the Ph.D. dissertation of the lead writer at Universitá Degli Studi Di Napoli “Federico II,” initiated while she was a visiting scholar at the University of Cincinnati. The writers thank Robert Janke and Regan Murray of the U.S. EPA, and Dominic Boccelli of the University of Cincinnati, for sharing their opinions and expertise on contamination warning systems. This research was partially funded by National Science Foundation under Grant No. NSFCMS-0540289, and partially funded by the U.S. Environmental Protection Agency through the Office of Research and Development that managed, and participated in the research described here under a contract with the University of Cincinnati (Pegasus Contract No. UNSPECIFIEDEP-C-05–056, Work Assignment WA 3–06). The views expressed in this paper are those of the writers and do not necessarily reflect the views or policies of the USEPA. Mention of trade names or commercial products does not constitute endorsement or recommendation for use.
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Information
Published In
Journal of Water Resources Planning and Management
Volume 136 • Issue 4 • July 2010
Pages: 444 - 453
Copyright
© 2010 ASCE.
History
Received: Dec 10, 2008
Accepted: Sep 25, 2009
Published online: Oct 2, 2009
Published in print: Jul 2010
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