Source Contamination Detection Using Novel Search Space Reduction Coupled with Optimization Technique
Publication: Journal of Water Resources Planning and Management
Volume 147, Issue 2
Abstract
Contaminant intrusion in a water distribution network is an important concern because it can have hazardous consequences for the population. Reacting immediately is crucial to prevent or reduce the further propagation of contamination. In terms of contamination scenario characteristics, optimization is researched extensively as a valuable methodology to provide information. This work presented a procedure preceding the optimization which considerably reduces the search space for a potential contaminant source location. For each suspect node, a simulation is conducted with unrealistically high contaminant concentration injected throughout the whole simulation. If the sensors do not register contamination in a subsequent scenario, then that node can be eliminated as a possible contaminant source. The methodology is applicable for both single and multiple contaminant injection nodes. This approach was investigated in multiple benchmark networks and for different sensor placements in the literature. By coupling the proposed search space reduction method with an optimization approach, a novel efficient methodology for contamination source detection was presented.
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request. Available data include the Python script for the search space reduction method, the Python script for PSO, the Python script for the GA, and the Epanet2 benchmark networks.
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Published In
Journal of Water Resources Planning and Management
Volume 147 • Issue 2 • February 2021
Copyright
© 2020 American Society of Civil Engineers.
History
Received: Dec 21, 2019
Accepted: Jul 27, 2020
Published online: Nov 20, 2020
Published in print: Feb 1, 2021
Discussion open until: Apr 20, 2021
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