TY - GEN
T1 - A bayesian approach for probabilistic contamination source identification
AU - Yang, Xueyao
AU - Boccelli, Dominic L.
AU - De Sanctis, Annamaria E.
PY - 2011
Y1 - 2011
N2 - 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.
AB - 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.
KW - Bayesian analysis
KW - Probability
KW - Water distribution systems
KW - Water pollution
UR - http://www.scopus.com/inward/record.url?scp=79960397779&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=79960397779&partnerID=8YFLogxK
U2 - 10.1061/41173(414)33
DO - 10.1061/41173(414)33
M3 - Conference contribution
AN - SCOPUS:79960397779
SN - 9780784411735
T3 - World Environmental and Water Resources Congress 2011: Bearing Knowledge for Sustainability - Proceedings of the 2011 World Environmental and Water Resources Congress
SP - 304
EP - 313
BT - World Environmental and Water Resources Congress 2011
T2 - World Environmental and Water Resources Congress 2011: Bearing Knowledge for Sustainability
Y2 - 22 May 2011 through 26 May 2011
ER -