Chapter
Apr 26, 2012
Adaptive Bayesian Contaminant Source Characterization in Water Distribution Systems via a Parallel Implementation of Markov Chain Monte Carlo (MCMC)
Authors: Hui Wanga and Kenneth W. HarrisonAuthor Affiliations
Publication: World Environmental and Water Resources Congress 2010: Challenges of Change
Abstract
A Markov Chain Monte Carlo (MCMC) implementation of Bayesian analysis can yield a probabilistic source characterization conditioned on available sensor data. Harrison and Wang (2009) proposed an MCMC approach tailored for water distribution systems with an emphasis on the proposal distribution, often the most problematic component of MCMC. Here, we present an adaptive framework of taking mobile sensor measurements to further complement the fixed sensors. It is implemented in a computationally feasible parallel implementation of the approach. The results demonstrate the potential of Bayesian analysis and the MCMC method for contaminant event management.
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© 2010 American Society of Civil Engineers.
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Published online: Apr 26, 2012
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Hui Wanga
North Carolina State University, Campus Box 7908, Raleigh, NC 27695-7908
Kenneth W. Harrison
North Carolina State University, Campus Box 7908, Raleigh, NC 27695-7908
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