Sensor placement in water distribution networks using centrality-guided multi-objective optimisation

Abstract
This paper introduces a multi-objective optimisation approach for the challenging problem of efficient sensor placement in water distribution networks for contamination detection. An important question is, how to identify the minimal number of required sensors without losing the capacity to monitor the system as a whole. In this study, we adapted the NSGA-II multi-objective optimisation method by applying centrality mutation. The approach, with two objectives, namely the minimisation of Expected Time of Detection and maximisation of Detection Network Coverage (which computes the number of detected water contamination events), is tested on a moderate-sized benchmark problem (129 nodes). The resulting Pareto front shows that detection network coverage can improve dramatically by deploying only a few sensors (e.g. increase from one sensor to three sensors). However, after reaching a certain number of sensors (e.g. 20 sensors), the effectiveness of further increasing the number of sensors is not apparent. Further, the results confirm that 40–45 sensors (i.e. 31 35% of the total number of nodes) will be sufficient for fully monitoring the benchmark network, i.e. for detection of any contaminant intrusion event no matter where it appears in the network.
Main Authors
Format
Articles Research article
Published
2024
Series
Subjects
Publication in research information system
Publisher
IWA Publishing
The permanent address of the publication
https://urn.fi/URN:NBN:fi:jyu-202310276912Use this for linking
Review status
Peer reviewed
ISSN
1464-7141
DOI
https://doi.org/10.2166/hydro.2023.057
Language
English
Published in
Journal of Hydroinformatics
Citation
  • Diao, K., Emmerich, M., Lan, J., Yevseyeva, I., & Sitzenfrei, R. (2024). Sensor placement in water distribution networks using centrality-guided multi-objective optimisation. Journal of Hydroinformatics, 25(6), 2291-2303. https://doi.org/10.2166/hydro.2023.057
License
CC BY 4.0Open Access
Additional information about funding
The contribution of the University of Innsbruck was supported by the project RESIST (FO999886338) which is funded by the Austrian security research programme KIRAS of the Federal Ministry of Agriculture, Regions and Tourism (BMLRT).
Copyright© 2023 the Authors

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