A bayesian approach for probabilistic contamination source identification

Xueyao Yang, Dominic L. Boccelli, Annamaria E. De Sanctis

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

3 Scopus citations

Abstract

Drinking water distribution system models have been prominent in the development and implementation of contaminant warning systems. This study proposes a new probabilistic contaminant source identification algorithm using a Beta-Binomial conjugate pair framework to identify contaminant sources in water distribution system, and compares the performance of this algorithm to a previous study using a discrete probability representation based on Bayes' Rule. The evaluation of the performance associated with the two algorithms was conducted using a simulation study with a conservative "chemical injection" event within a small distribution system network. Preliminary results showed that while the Bayes' Rule approach responded faster, the algorithm can quickly become insensitive to changes in the event detection signal. However, the Beta-Binomial approach appeared to better represent the true source location and injection time.

Original languageEnglish (US)
Title of host publicationWorld Environmental and Water Resources Congress 2011
Subtitle of host publicationBearing Knowledge for Sustainability - Proceedings of the 2011 World Environmental and Water Resources Congress
Pages304-313
Number of pages10
DOIs
StatePublished - 2011
Externally publishedYes
EventWorld Environmental and Water Resources Congress 2011: Bearing Knowledge for Sustainability - Palm Springs, CA, United States
Duration: May 22 2011May 26 2011

Publication series

NameWorld Environmental and Water Resources Congress 2011: Bearing Knowledge for Sustainability - Proceedings of the 2011 World Environmental and Water Resources Congress

Other

OtherWorld Environmental and Water Resources Congress 2011: Bearing Knowledge for Sustainability
Country/TerritoryUnited States
CityPalm Springs, CA
Period5/22/115/26/11

Keywords

  • Bayesian analysis
  • Probability
  • Water distribution systems
  • Water pollution

ASJC Scopus subject areas

  • Environmental Science(all)

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