🤖 AI Summary
This study addresses the critical yet underexplored impact of sensor placement on the performance of graph neural networks (GNNs) for leak detection in water distribution networks. To fill the gap in systematic optimization approaches, the authors propose a novel sensor placement strategy that leverages PageRank centrality to prioritize nodes based on their topological importance—a first in this domain. The work systematically evaluates how different placement schemes influence GNN performance across pressure reconstruction, forecasting, and leak detection tasks. Integrating EPANET-based hydraulic simulations with GNN models, experiments on the Net1 benchmark network demonstrate that the proposed method significantly enhances pressure reconstruction accuracy, prediction stability, and leak detection precision. These findings offer a new topology-aware perspective for optimizing sensor deployment in intelligent water infrastructure.
📝 Abstract
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.