Prediction of Environmental Conditions for Maritime Navigation using a Network of Sensors: A Practical Application of Graph Neural Networks
Resumo
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
Referências
Battaglia, P. W., Hamrick, J. B., Bapst, V., Sanchez-Gonzalez, A., Zambaldi, V., Malinowski, M., Tacchetti, A., Raposo, D., Santoro, A., Faulkner, R., Gulcehre, C., Song, F., Ballard, A., Gilmer, J., Dahl, G., Vaswani, A., Allen, K., Nash, C., Langston, V., Dyer, C., Heess, N., Wierstra, D., Kohli, P., Botvinick, M., Vinyals, O., Li, Y., and Pascanu, R. Relational inductive biases, deep learning, and graph networks. Tech. rep., arXiv:1806.01261, 2018.
Brockwell, P. J. and Davis, R. A. Time Series: Theory and Methods. Springer, 1987.
Buuren, S. and Groothuis-Oudshoorn, K. MICE: Multivariate imputation by chained equations in R. Journal of statistical Software 45 (3): 1–67, 2011.
Gori, M., Monfardini, G., and Scarselli, F. A new model for learning in graph domains. In IEEE International Joint Conference on Neural Networks. Vol. 2. pp. 729–734, 2005.
Hochreiter, S. and Schmidhuber, J. Long short-term memory. Neural computation vol. 9, pp. 1735–80, 1997.
PIANC MarCom. Use of hydro/meteo information for port access and operations. Tech. rep., WG 117 Report 117, 2012.
Scarselli, F., Gori, M., Tsoi, A., Hagenbuchner, M., and Monfardini, G. The graph neural network model. IEEE Transactions on Neural Networks 20 (1): 61–80, 2009.
Sperduti, A. and Starita, A. Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8 (3): 714–735, 1997.
Wang, X., Girshick, R., Gupta, A., and He, K. Non-local neural networks. In IEEE Conference on Computer Vision and Pattern Recognition. pp. 7794–7803, 2018.
Wu, M., Stefanakos, C., Gao, Z., and Haver, S. Prediction of short-term wind and wave conditions for marine operations using a multi-step-ahead decomposition-ANFIS model and quantification of its uncertainty. Ocean Engineering vol. 188, pp. 106300, 2019.
Brockwell, P. J. and Davis, R. A. Time Series: Theory and Methods. Springer, 1987.
Buuren, S. and Groothuis-Oudshoorn, K. MICE: Multivariate imputation by chained equations in R. Journal of statistical Software 45 (3): 1–67, 2011.
Gori, M., Monfardini, G., and Scarselli, F. A new model for learning in graph domains. In IEEE International Joint Conference on Neural Networks. Vol. 2. pp. 729–734, 2005.
Hochreiter, S. and Schmidhuber, J. Long short-term memory. Neural computation vol. 9, pp. 1735–80, 1997.
PIANC MarCom. Use of hydro/meteo information for port access and operations. Tech. rep., WG 117 Report 117, 2012.
Scarselli, F., Gori, M., Tsoi, A., Hagenbuchner, M., and Monfardini, G. The graph neural network model. IEEE Transactions on Neural Networks 20 (1): 61–80, 2009.
Sperduti, A. and Starita, A. Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8 (3): 714–735, 1997.
Wang, X., Girshick, R., Gupta, A., and He, K. Non-local neural networks. In IEEE Conference on Computer Vision and Pattern Recognition. pp. 7794–7803, 2018.
Wu, M., Stefanakos, C., Gao, Z., and Haver, S. Prediction of short-term wind and wave conditions for marine operations using a multi-step-ahead decomposition-ANFIS model and quantification of its uncertainty. Ocean Engineering vol. 188, pp. 106300, 2019.
Publicado
20/10/2020
Como Citar
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: https://doi.org/10.5753/kdmile.2020.11981.
