TY - JOUR

T1 - Contaminant source identification in water distribution networks

T2 - A Bayesian framework

AU - Jerez, D. J.

AU - Jensen, H. A.

AU - Beer, M.

AU - Broggi, M.

N1 - Funding Information: The research reported here was supported in part by CONICYT (National Commission for Scientific and Technological Research) under Grant No. 1200087. Also, this research has been supported by CONICYT and DAAD under CONICYT-PFCHA/Doctorado Acuerdo Bilateral DAAD Becas Chile/2018–62180007. In addition, this research has been partially supported by DFG (German Research Foundation) under Grant No.BE 2570/3–1 and BR 5446/1–1. These supports are gratefully acknowledged by the authors.

PY - 2021/10

Y1 - 2021/10

N2 - This work presents a Bayesian model updating approach for handling contaminant source characterization problems in the context of water distribution networks. The problem is formulated in a Bayesian model class selection framework where each model class represents a possible contaminant event. The parameters of each model class characterize the contaminant mass inflow over time in terms of its intensity and starting time. The class with the highest posterior probability is interpreted as the most plausible location for the contaminant injection. The evidences of the model classes are estimated using the transitional Markov chain Monte Carlo (TMCMC) method. The approach provides additional insight into the current network state in terms of posterior samples of the parameters that describe the contaminant event. The effectiveness of the proposed identification framework is illustrated by applying the contaminant source detection approach to a couple of water distribution systems.

AB - This work presents a Bayesian model updating approach for handling contaminant source characterization problems in the context of water distribution networks. The problem is formulated in a Bayesian model class selection framework where each model class represents a possible contaminant event. The parameters of each model class characterize the contaminant mass inflow over time in terms of its intensity and starting time. The class with the highest posterior probability is interpreted as the most plausible location for the contaminant injection. The evidences of the model classes are estimated using the transitional Markov chain Monte Carlo (TMCMC) method. The approach provides additional insight into the current network state in terms of posterior samples of the parameters that describe the contaminant event. The effectiveness of the proposed identification framework is illustrated by applying the contaminant source detection approach to a couple of water distribution systems.

KW - Bayesian model updating

KW - Contaminant source identification

KW - Model class selection

KW - Water distribution systems

UR - http://www.scopus.com/inward/record.url?scp=85102880194&partnerID=8YFLogxK

U2 - 10.1016/j.ymssp.2021.107834

DO - 10.1016/j.ymssp.2021.107834

M3 - Article

AN - SCOPUS:85102880194

VL - 159

JO - Mechanical Systems and Signal Processing

JF - Mechanical Systems and Signal Processing

SN - 0888-3270

M1 - 107834

ER -