Spatial Graph Convolution Neural Networks for Water Distribution Systems

• URL: http://arxiv.org/abs/2211.09587v1
• Date: Thu, 17 Nov 2022 15:32:11 GMT
• Title: Spatial Graph Convolution Neural Networks for Water Distribution Systems
• Authors: Inaam Ashraf and Luca Hermes and Andr\'e Artelt and Barbara Hammer
• Abstract summary: We investigate the task of missing value estimation in graphs as given by water distribution systems (WDS) based on sparse signals. The underlying graphs have a comparably low node degree and high diameter, while information in the graph is globally relevant. We propose a specific architecture based on message passing which displays excellent results for a number of benchmark tasks in the WDS domain.
• Score: 6.125017875330933
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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.

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