We investigate the task of missing value estimation in graphs as given by water distribution systems (WDS) based on sparse signals as a representative machine learning challenge in the domain of critical infrastructure. The underlying graphs have a comparably low node degree and high diameter, while information in the graph is globally relevant, hence graph neural networks face the challenge of long term dependencies. We propose a specific architecture based on message passing which displays excellent results for a number of benchmark tasks in the WDS domain. Further, we investigate a multi-hop variation, which requires considerably less resources and opens an avenue towards big WDS graphs.
CITATION STYLE
Ashraf, I., Hermes, L., Artelt, A., & Hammer, B. (2023). Spatial Graph Convolution Neural Networks for Water Distribution Systems. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 13876 LNCS, pp. 29–41). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-30047-9_3
Mendeley helps you to discover research relevant for your work.