Details
| Original language | English |
|---|---|
| Article number | 107834 |
| Journal | Mechanical Systems and Signal Processing |
| Volume | 159 |
| Early online date | 20 Mar 2021 |
| Publication status | Published - Oct 2021 |
Abstract
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.
Keywords
- Bayesian model updating, Contaminant source identification, Model class selection, Water distribution systems
ASJC Scopus subject areas
- Engineering(all)
- Control and Systems Engineering
- Computer Science(all)
- Signal Processing
- Engineering(all)
- Civil and Structural Engineering
- Engineering(all)
- Aerospace Engineering
- Engineering(all)
- Mechanical Engineering
- Computer Science(all)
- Computer Science Applications
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In: Mechanical Systems and Signal Processing, Vol. 159, 107834, 10.2021.
Research output: Contribution to journal › Article › Research › peer review
}
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 -