Sensor Placement in Water Distribution Networks Using Centrality Guided Multi-objective Optimisation

Diao, Kegong
Emmerich, Michael
Lan, Jacob
Iryna, Yevseyeva
Sitzenfrei, Robert
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International Water Association
Peer reviewed
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.
open access article
Centrality, Contamination Detection, Early Warning System, Optimisation, Sensor, Water Distribution Networks
Diao, K., Emmerich, M., Lan, J., Yevseyeva,, I. and Sitzenfrei, R. (2023) Sensor placement in water distribution networks using centrality-guided multi-objective optimisation. Journal of Hydroinformatics, jh2023057
Research Institute
Institute of Energy and Sustainable Development (IESD)