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2023 | OriginalPaper | Buchkapitel

Spatial Graph Convolution Neural Networks for Water Distribution Systems

verfasst von : Inaam Ashraf, Luca Hermes, André Artelt, Barbara Hammer

Erschienen in: Advances in Intelligent Data Analysis XXI

Verlag: Springer Nature Switzerland

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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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Vorheriges Kapitel Transferable Deep Metric Learning for Clustering

Nächstes Kapitel Data-Centric Perspective on Explainability Versus Performance Trade-Off

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Titel: Spatial Graph Convolution Neural Networks for Water Distribution Systems
verfasst von: Inaam Ashraf
Luca Hermes
André Artelt
Barbara Hammer
Verlag: Springer Nature Switzerland
Buch: Advances in Intelligent Data Analysis XXI
Print ISBN: 978-3-031-30046-2

Electronic ISBN: 978-3-031-30047-9

Copyright-Jahr: 2023
DOI: https://doi.org/10.1007/978-3-031-30047-9_3

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