The impact of sensor placement on graph-neural-network-based leakage detection
This addresses a practical challenge for water utilities, but it is incremental as it builds on existing graph neural network approaches.
The study tackled the problem of sensor placement for leakage detection in water distribution networks, finding that a novel PageRank-Centrality-based method substantially impacts performance on the EPANET Net1 dataset.
Sensor placement for leakage detection in water distribution networks is an important and practical challenge for water utilities. Recent work has shown that graph neural networks can estimate and predict pressures and detect leaks, but their performance strongly depends on the available sensor measurements and configurations. In this paper, we investigate how sensor placement influences the performance of GNN-based leakage detection. We propose a novel PageRank-Centrality-based sensor placement method and demonstrate that it substantially impacts reconstruction, prediction, and leakage detection on the EPANET Net1.