[2603.24076] The impact of sensor placement on graph-neural-network-based leakage detection

Computer Science > Machine Learning arXiv:2603.24076 (cs) [Submitted on 25 Mar 2026] Title:The impact of sensor placement on graph-neural-network-based leakage detection Authors:J.J.H. van Gemert, V. Breschi, D.R. Yntema, K.J. Keesman, M. Lazar View a PDF of the paper titled The impact of sensor placement on graph-neural-network-based leakage detection, by J.J.H. van Gemert and 4 other authors View PDF HTML (experimental) 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. Subjects: Machine Learning (cs.LG); Systems and Control (eess.SY) Cite as: arXiv:2603.24076 [cs.LG]   (or arXiv:2603.24076v1 [cs.LG] for this version)   https://doi.org/10.48550/arXiv.2603.24076 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Jarne Gemert Van [view email] [v1] Wed, 25 Mar 2026 08:31:37 UTC (738 KB) Full-text links: Access Paper: View a PDF of the paper titled The impac...