Prediction of Environmental Conditions for Maritime Navigation using a Network of Sensors: A Practical Application of Graph Neural Networks

  • Caio Netto Universidade de São Paulo
  • Eduardo Tannuri Universidade de São Paulo
  • Denis Mauá Universidade de São Paulo
  • Fábio Cozman Universidade de São Paulo


This paper describes a real application of graphical neural networks (GNNs) in the dynamic estimation of spatially distributed buoys that are of central importance in maritime navigation. We describe the techniques we used to process both data and background knowledge about the domain, indicating why GNNs are particularly well suited for this sort of task. We report our empirical results, demonstrating that GNNs profitably use the avaible relational structure.

Palavras-chave: forecasting, graph neural networks, relational learning, time series


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NETTO, Caio; TANNURI, Eduardo; MAUÁ, Denis; COZMAN, Fábio. Prediction of Environmental Conditions for Maritime Navigation using a Network of Sensors: A Practical Application of Graph Neural Networks. In: SYMPOSIUM ON KNOWLEDGE DISCOVERY, MINING AND LEARNING (KDMILE), 8. , 2020, Evento Online. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2020 . p. 233-240. ISSN 2763-8944. DOI: