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Spatial Graph Convolution Neural Networks for Water Distribution Systems

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Advances in Intelligent Data Analysis XXI (IDA 2023)

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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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Notes

  1. 1.

    https://github.com/HammerLabML/GCNs_for_WDS.

  2. 2.

    https://engineering.exeter.ac.uk/research/cws/resources/benchmarks/#a8.

  3. 3.

    https://www.batadal.net/data.html.

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Acknowledgements

We gratefully acknowledge funding from the European Research Council (ERC) under the ERC Synergy Grant Water-Futures (Grant agreement No. 951424). This research was also supported by the research training group “Dataninja” (Trustworthy AI for Seamless Problem Solving: Next Generation Intelligence Joins Robust Data Analysis) funded by the German federal state of North Rhine-Westphalia, and by funding from the VW-Foundation for the project IMPACT funded in the frame of the funding line AI and its Implications for Future Society.

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Authors and Affiliations

  1. Center for Cognitive Interaction Technology, Bielefeld University, Bielefeld, Germany

    Inaam Ashraf, Luca Hermes, André Artelt & Barbara Hammer

Authors
  1. Inaam Ashraf
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  2. Luca Hermes
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  3. André Artelt
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  4. Barbara Hammer
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Correspondence to Inaam Ashraf .

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Editors and Affiliations

  1. Université de Caen Normandie, Caen, France

    Bruno Crémilleux

  2. Eindhoven University of Technology, Eindhoven, The Netherlands

    Sibylle Hess

  3. UCLouvain, Louvain-la-Neuve, Belgium

    Siegfried Nijssen

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Ashraf, I., Hermes, L., Artelt, A., Hammer, B. (2023). Spatial Graph Convolution Neural Networks for Water Distribution Systems. In: Crémilleux, B., Hess, S., Nijssen, S. (eds) Advances in Intelligent Data Analysis XXI. IDA 2023. Lecture Notes in Computer Science, vol 13876. Springer, Cham. https://doi.org/10.1007/978-3-031-30047-9_3

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  • DOI: https://doi.org/10.1007/978-3-031-30047-9_3

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