Bayesian Belief Networks to Integrate Monitoring Evidence of Water Distribution System Contamination
Publication: Journal of Water Resources Planning and Management
Volume 132, Issue 4
Abstract
A Bayesian belief network (BBN) methodology is proposed for combining evidence to better characterize contamination events and reduce false positive sensor detections in drinking water distribution systems. A BBN is developed that integrates sensor data with other validating evidence of contamination scenarios. This network is used to graphically express the causal relationships between events such as operational changes or a true contaminant release and consequent observable evidence in an example distribution system. In the BBN methodology proposed here, multiple computer simulations of contaminant transport are used to estimate the prior probabilities of a positive sensor detection. These simulations are run over multiple combinations of possible source locations and initial mass injections for a conservative solute. This approach provides insight into the effect of uncertainties in source mass and location on the detection probability of the sensors. In addition, the simulations identify the upstream nodes that are more likely to result in positive detections. The BBN incorporates the probabilities that result from these simulations, and the network is updated to reflect three demonstration scenarios—a false positive and two true positive sensor detections.
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Acknowledgments
This research was supported by the Office of Naval Research Grant No. ONRN00014-04-1-0437 through the Technology Research, Education and Commercialization Center (TRECC). The writers would like to acknowledge Tim Perkins of the Army Corps of Engineers Construction Engineering Research Laboratory (CERL) for his efforts in creating the distribution system model used in this study. Network modeling software used in this study included WaterGEMS from Haestad Methods and EPANET from the United States Environmental Protection Agency. The writers would also like to acknowledge Fabio Cozman, creator of the Bayesian belief network modeling software JavaBayes.
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Information & Authors
Information
Published In
Journal of Water Resources Planning and Management
Volume 132 • Issue 4 • July 2006
Pages: 234 - 241
Copyright
© 2006 ASCE.
History
Received: Aug 22, 2005
Accepted: Dec 30, 2005
Published online: Jul 1, 2006
Published in print: Jul 2006
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