@inproceedings{f2b713f0159b415caa2220c3472897e3,
title = "A comparison between two Bayesian approaches for probabilistic contaminant source identification",
abstract = "Drinking water distribution system models have been increasingly utilized in the development and implementation of contaminant warning systems. This study proposes a new Bayesian approach for probabilistic contamination source identification using a Beta-Binomial conjugate pair framework to identify contaminant source locations and times, and compares the performance of this algorithm to previous work based on a Bayes' Rule approach. The evaluation of the performance associated with the two algorithms was conducted by a simple comparison as well as a simulation study in terms of a conservative {"}chemical injection{"} event through a small distribution system network. Results from the simple comparison showed that the Beta-Binomial approach was more {"}responsive{"} to changes in sensor signals. In terms of the chemical intrusion event, the Beta-Binomial approach was more selective than the Bayes' Rule approach in identifying potential node-time pairs.",
keywords = "Bayes' Rule, Beta-Binomial, Source identification, Water security",
author = "Xueyao Yang and Boccelli, {Dominic L.} and {De Sanctis}, {Annamaria E.}",
year = "2011",
language = "English (US)",
isbn = "0953914089",
series = "Urban Water Management: Challenges and Oppurtunities - 11th International Conference on Computing and Control for the Water Industry, CCWI 2011",
publisher = "Centre for Water Systems",
booktitle = "Urban Water Management",
note = "11th International Conference on Computing and Control for the Water Industry, CCWI 2011 ; Conference date: 05-09-2011 Through 07-09-2011",
}