Accurate and Optimal Sensor Placement for Source Identification of Water Distribution Networks
Publication: Journal of Water Resources Planning and Management
Volume 143, Issue 8
Abstract
The problem of sensor placement for early-warning detection systems is a topical issue for industry and utilities who want to equip their networks with such technology. Solving the problem entails finding the best sensor locations that optimize a criterion such as detection rate or time to detection. Few methods exist concerning sensor placement that optimize the result of a source identification method. This paper fills the gap by coupling an adjoint source identification method and a Monte Carlo sensor placement algorithm. The first one is treated through the use of a backtracking algorithm. It uses binary responses at sensors to calculate the ranked list of potential contamination location nodes and contamination times. A criterion is then defined based on the source identification accuracy and specificity. Finally, two optimizing methods that maximize this criterion are proposed: a greedy algorithm and a local search algorithm, which are both coupled with a Monte Carlo method to give the locations of sensors that are the best suited for allocating the source of a contamination. These methods are tested on a 2,500-node network to evaluate their efficiency.
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Acknowledgments
The authors wish to acknowledge the following institutes for allowing use of their computation cluster to carry out the simulations performed in this project: Mésocentre de Calcul Intensif Aquitain (MCIA), Avakas supercomputer. The research part of the project is supported by the German Federal Ministry of Education and Research (BMBF; Project 13N12180) and by the French Agence Nationale de la Recherche (ANR; Project ANR-11-SECU-006).
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Published In
Journal of Water Resources Planning and Management
Volume 143 • Issue 8 • August 2017
Copyright
©2017 American Society of Civil Engineers.
History
Received: Feb 22, 2016
Accepted: Jan 18, 2017
Published online: May 3, 2017
Published in print: Aug 1, 2017
Discussion open until: Oct 3, 2017
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