Bayesian Update Method for Contaminant Source Characterization in Water Distribution Systems
Publication: Journal of Water Resources Planning and Management
Volume 139, Issue 1
Abstract
Bayesian analysis has application to probabilistic source characterization in water distribution systems. A new implementation of Markov-chain Monte Carlo (MCMC) for this problem is described. The solution addresses the discrete nature of water distribution networks that precludes the application of MCMC methods of general applicability that have been reported elsewhere in the water resources literature. The method is applied to a hypothetical network that has been used by others to test source identification methods. The likelihood function, a key component of Bayes’ rule, is evaluated using a Monte Carlo–based stochastic water-demand model. The results reinforce the need to address the multiple sources of uncertainty in the source characterization, including the stochastic variation of water demand. Further research is needed to make the approach feasible in operational environments. Limitations of the approach and future research directions are discussed.
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Acknowledgments
This work is supported by the National Science Foundation (NSF) under Award No. 0849064 under the Dynamic Data-Driven Application Systems (DDDAS) program. Any opinions, findings, and conclusions expressed in this paper are those of the authors and do not necessarily reflect the views of the NSF. The authors would also like to thank the anonymous reviewers for valued input into the paper.
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Information
Published In
Journal of Water Resources Planning and Management
Volume 139 • Issue 1 • January 2013
Pages: 13 - 22
Copyright
© 2013 American Society of Civil Engineers.
History
Received: Jul 3, 2011
Accepted: Dec 16, 2011
Published online: Dec 21, 2011
Published in print: Jan 1, 2013
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