Learning the spatiotemporal relationship between wind and significant wave height using deep learning

Type Article
Date 2023-02-15
Language English
Author(s) Obakrim SaidORCID1, 2, Monbet Valerie1, Raillard NicolasORCID2, Ailliot PierreORCID3
Affiliation(s) 1 : Univ Rennes, CNRS, IRMAR – UMR 6625, Rennes, France
2 : IFREMER, RDT, F-29280 Plouzané, France
3 : Laboratoire de Mathématiques de Bretagne Atlantique, Université de Brest, Brest, France
Source Environmental Data Science (2634-4602) (Cambridge University Press), 2023-02-15 , Vol. 2 , N. E5 , P. 8p.
DOI 10.1017/eds.2022.35
Keyword(s) Convolutional neural networks, long short-term memory, significant wave height, wind fields

Ocean wave climate has a significant impact on near-shore and off-shore human activities, and its characterization can help in the design of ocean structures such as wave energy converters and sea dikes. Therefore, engineers need long time series of ocean wave parameters. Numerical models are a valuable source of ocean wave data; however, they are computationally expensive. Consequently, statistical and data-driven approaches have gained increasing interest in recent decades. This work investigates the spatiotemporal relationship between North Atlantic wind and significant wave height (Hs) at an off-shore location in the Bay of Biscay, using a two-stage deep learning model. The first step uses convolutional neural networks to extract the spatial features that contribute to Hs. Then, long short-term memory is used to learn the long-term temporal dependencies between wind and waves.

Licence CC-BY
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Publisher's official version 8 1 MB Open access
Preprint - arXiv:2205.13325v1 7 445 KB Open access
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