Contaminant source identification in water distribution networks

A Bayesian framework

authored by

D. J. Jerez, H. A. Jensen, M. Beer, M. Broggi

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.

Organisation(s)

Institute for Risk and Reliability

External Organisation(s)

University of Liverpool
Universidad Tecnica Federico Santa Maria
Tongji University

Type

Article

Journal

Mechanical Systems and Signal Processing

Volume

159

ISSN

0888-3270

Publication date

10.2021

Publication status

Published

Peer reviewed

Yes

ASJC Scopus subject areas

Control and Systems Engineering, Signal Processing, Civil and Structural Engineering, Aerospace Engineering, Mechanical Engineering, Computer Science Applications

Electronic version(s)

https://doi.org/10.1016/j.ymssp.2021.107834 (Access: Closed)