Chapter
Apr 26, 2012
A Markov Chain Monte Carlo Implementation of Bayesian Contaminant Source Characterization in Water Distribution Systems under Stochastic Demands
Authors: Kenneth W. Harrison and Hui WangAuthor Affiliations
Publication: World Environmental and Water Resources Congress 2009: Great Rivers
Abstract
Bayesian analysis is applied to the problem of probabilistic source characterization in water distribution systems. An implementation of the Markov Chain Monte Carlo (MCMC) method is tailored to water distribution systems. The likelihood function, a key component of Bayes' rule, is approximated using a Monte Carlo-based stochastic water demand model. Limitations of the approach are also discussed.
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Information
Published In
World Environmental and Water Resources Congress 2009: Great Rivers
Pages: 1 - 11
Copyright
© 2009 American Society of Civil Engineers.
History
Published online: Apr 26, 2012
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ASCE Technical Topics:
- Analysis (by type)
- Bayesian analysis
- Distribution functions
- Engineering fundamentals
- Environmental engineering
- Markov process
- Mathematical functions
- Mathematics
- Methodology (by type)
- Monte Carlo method
- Numerical methods
- Pollutants
- Probability
- Probability distribution
- Statistical analysis (by type)
- Stochastic processes
- Water and water resources
- Water management
- Water supply
- Water supply systems
Authors
Affiliations
Kenneth W. Harrison
Earth System Science Interdisciplinary Center, College Park, MD 20740-3823 and Hydrological Sciences Branch, NASA Goddard Space Right Center, Greenbelt, MD, 20771
Hui Wang
North Carolina State University, Campus Box 7908, Raleigh, NC 27695-7908
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Terms of Use: ASCE Library Cards are for individual, personal use only. Reselling, republishing, or forwarding the materials to libraries or reading rooms is prohibited.