Real time assessment of drinking water systems using a dynamic Bayesian network

W. J. Dawsey, Barbara S Minsker, E. Amir

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publicationRestoring Our Natural Habitat - Proceedings of the 2007 World Environmental and Water Resources Congress
StatePublished - Dec 1 2007
Event2007 World Environmental and Water Resources Congress: Restoring Our Natural Habitat - Tampa, FL, United States
Duration: May 15 2007May 19 2007

Publication series

NameRestoring Our Natural Habitat - Proceedings of the 2007 World Environmental and Water Resources Congress

Other

Other2007 World Environmental and Water Resources Congress: Restoring Our Natural Habitat
CountryUnited States
CityTampa, FL
Period5/15/075/19/07

ASJC Scopus subject areas

  • Environmental Engineering
  • Water Science and Technology

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  • Cite this

    Dawsey, W. J., Minsker, B. S., & Amir, E. (2007). Real time assessment of drinking water systems using a dynamic Bayesian network. In Restoring Our Natural Habitat - Proceedings of the 2007 World Environmental and Water Resources Congress (Restoring Our Natural Habitat - Proceedings of the 2007 World Environmental and Water Resources Congress).