Bayesian Approach for Real-Time Probabilistic Contamination Source Identification
Publication: Journal of Water Resources Planning and Management
Volume 140, Issue 8
Abstract
Drinking water distribution system models have been increasingly utilized in the development and implementation of contaminant warning systems. This study proposes a Bayesian approach for probabilistic contamination source identification using a beta-binomial conjugate pair framework to identify contaminant source locations and times and compares the performance of this algorithm to previous work based on a Bayes’ rule approach. The proposed algorithm is capable of directly assigning a probability to a potential source location and updating the probability through the use of a backtracking algorithm and Bayesian statistics. The evaluation of the performance associated with the two algorithms was conducted by a simple comparison, as well as a simulation study in terms of a conservative chemical intrusion event through both a small skeletonized network and a large all-pipe distribution system network. Results from the simple comparison showed that the beta-binomial approach was more responsive to changes in sensor signals. In terms of intrusion events, the beta-binomial approach was more selective than the Bayes’ rule approach in identifying potential source node–time pairs and provided the flexibility to account for multiple possible injection locations.
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Acknowledgments
The authors sincerely appreciate the help of Dr. Feng Shang in the implementation of the particle backtracking algorithm as well as Mr. Robert Janke in running sensor placement results using TEVA-SPOT. The authors also would like to gratefully acknowledge the funding support provided by the CMMI Directorate, Civil Infrastructure Systems (NSF) through grant number #0900713.
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Published In
Journal of Water Resources Planning and Management
Volume 140 • Issue 8 • August 2014
Copyright
© 2014 American Society of Civil Engineers.
History
Received: Aug 18, 2012
Accepted: Jun 3, 2013
Published online: Jun 5, 2013
Published in print: Aug 1, 2014
Discussion open until: Sep 22, 2014
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