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Real Time Assessment of Drinking Water Systems Using a Dynamic Bayesian Network
| Content Provider | Semantic Scholar |
|---|---|
| Author | Dawsey, W. J. Minsker, Barbara S. Amir, Eyal |
| Copyright Year | 2007 |
| Abstract | This paper presents a methodology for real-time estimation of water distribution system state parameters using a dynamic Bayesian network to combine current observations with knowledge of past system behavior. The dynamic Bayesian network presented here allows the flexibility to model both discrete and continuous variables and represent causal relationships that exist within the distribution system. The posterior belief state can be inferred using a compact approximation algorithm that has been shown to contain inference errors. Simulations over stochastic variables are proposed to define the transition and observation models for the dynamic Bayesian network. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://reason.cs.uiuc.edu/eyal/papers/bayesian-water-wewrc07.pdf |
| Alternate Webpage(s) | http://www.cs.uiuc.edu/~eyal/papers/bayesian-water-wewrc07.pdf |
| Alternate Webpage(s) | https://static.aminer.org/pdf/PDF/000/263/419/application_of_bayesian_belief_network_to_groundwater_quality_assessment.pdf |
| Language | English |
| Access Restriction | Open |
| Subject Keyword | Alcohol consumption Approximation algorithm CNS disorder Causality Dynamic Bayesian network Inference Real-time clock Simulation preorder |
| Content Type | Text |
| Resource Type | Article |
