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

Authors :
Said Obakrim
Valérie Monbet
Nicolas Raillard
Pierre Ailliot
Source :
Environmental Data Science, Vol 2 (2023)
Publication Year :
2023
Publisher :
Cambridge University Press, 2023.

Abstract

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 ( $ {H}_s $ ) 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 $ {H}_s $ . Then, long short-term memory is used to learn the long-term temporal dependencies between wind and waves.

Details

Language :
English
ISSN :
26344602
Volume :
2
Database :
Directory of Open Access Journals
Journal :
Environmental Data Science
Publication Type :
Academic Journal
Accession number :
edsdoj.344e98ef7c764d0689e4b31b1f25274c
Document Type :
article
Full Text :
https://doi.org/10.1017/eds.2022.35