Optimal Sensor Placement in Water Distribution Networks Using Dynamic Prediction Graph Neural Networks
Sensors are a key component of water distribution networks due to their role in monitoring system variables. Specifically, water quality (WQ) sensors are utilized to measure chlorine concentrations in order to maintain water quality standards. However, the prohibitive costs of deploying these sensor...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-09-01
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| Series: | Engineering Proceedings |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2673-4591/69/1/171 |
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| Summary: | Sensors are a key component of water distribution networks due to their role in monitoring system variables. Specifically, water quality (WQ) sensors are utilized to measure chlorine concentrations in order to maintain water quality standards. However, the prohibitive costs of deploying these sensors constrain their ubiquitous use. As a result, WQ sensors are typically placed in a subset of junctions that are selected via an optimization process. This study presents a framework for optimizing WQ sensor placement to maximize chlorine concentration state estimation, that is, the inference of water quality parameters at unmonitored junctions based on the measurements from monitored junctions. This is performed by integrating a Dynamic Prediction Graph Neural Network (DP-GNN) model with a Genetic Algorithm (GA). The DP-GNN model is trained to predict chlorine concentrations at all junctions based on the measurements from sensors with different placements, whereas the GA uses these predictions to find the optimal sensor placement. The framework performance was tested by applying it to the C-town benchmark network, considering different numbers of sensors. The results demonstrated the impact of different sensor placements on the prediction accuracy of the DP-GNN model. Additionally, the results showed the framework’s ability to find the sensor placement that maximizes the chlorine concentration state estimation performance. |
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| ISSN: | 2673-4591 |