Learning the spatiotemporal relationship between wind and significant wave height using deep learning
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 valu...
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Cambridge University Press
2023
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ftarchimer:oai:archimer.ifremer.fr:94219 2023-11-12T04:22:10+01:00 Learning the spatiotemporal relationship between wind and significant wave height using deep learning Obakrim, Said Monbet, Valerie Raillard, Nicolas Ailliot, Pierre 2023-02-15 application/pdf https://archimer.ifremer.fr/doc/00830/94219/101601.pdf https://archimer.ifremer.fr/doc/00830/94219/105679.pdf https://doi.org/10.1017/eds.2022.35 https://archimer.ifremer.fr/doc/00830/94219/ eng eng Cambridge University Press https://archimer.ifremer.fr/doc/00830/94219/101601.pdf https://archimer.ifremer.fr/doc/00830/94219/105679.pdf doi:10.1017/eds.2022.35 https://archimer.ifremer.fr/doc/00830/94219/ info:eu-repo/semantics/openAccess restricted use Environmental Data Science (2634-4602) (Cambridge University Press), 2023-02-15 , Vol. 2 , N. E5 , P. 8p. Convolutional neural networks long short-term memory significant wave height wind fields text Article info:eu-repo/semantics/article 2023 ftarchimer https://doi.org/10.1017/eds.2022.35 2023-10-31T23:51:09Z 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. Article in Journal/Newspaper North Atlantic Archimer (Archive Institutionnelle de l'Ifremer - Institut français de recherche pour l'exploitation de la mer) Environmental Data Science 2 |
institution |
Open Polar |
collection |
Archimer (Archive Institutionnelle de l'Ifremer - Institut français de recherche pour l'exploitation de la mer) |
op_collection_id |
ftarchimer |
language |
English |
topic |
Convolutional neural networks long short-term memory significant wave height wind fields |
spellingShingle |
Convolutional neural networks long short-term memory significant wave height wind fields Obakrim, Said Monbet, Valerie Raillard, Nicolas Ailliot, Pierre Learning the spatiotemporal relationship between wind and significant wave height using deep learning |
topic_facet |
Convolutional neural networks long short-term memory significant wave height wind fields |
description |
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. |
format |
Article in Journal/Newspaper |
author |
Obakrim, Said Monbet, Valerie Raillard, Nicolas Ailliot, Pierre |
author_facet |
Obakrim, Said Monbet, Valerie Raillard, Nicolas Ailliot, Pierre |
author_sort |
Obakrim, Said |
title |
Learning the spatiotemporal relationship between wind and significant wave height using deep learning |
title_short |
Learning the spatiotemporal relationship between wind and significant wave height using deep learning |
title_full |
Learning the spatiotemporal relationship between wind and significant wave height using deep learning |
title_fullStr |
Learning the spatiotemporal relationship between wind and significant wave height using deep learning |
title_full_unstemmed |
Learning the spatiotemporal relationship between wind and significant wave height using deep learning |
title_sort |
learning the spatiotemporal relationship between wind and significant wave height using deep learning |
publisher |
Cambridge University Press |
publishDate |
2023 |
url |
https://archimer.ifremer.fr/doc/00830/94219/101601.pdf https://archimer.ifremer.fr/doc/00830/94219/105679.pdf https://doi.org/10.1017/eds.2022.35 https://archimer.ifremer.fr/doc/00830/94219/ |
genre |
North Atlantic |
genre_facet |
North Atlantic |
op_source |
Environmental Data Science (2634-4602) (Cambridge University Press), 2023-02-15 , Vol. 2 , N. E5 , P. 8p. |
op_relation |
https://archimer.ifremer.fr/doc/00830/94219/101601.pdf https://archimer.ifremer.fr/doc/00830/94219/105679.pdf doi:10.1017/eds.2022.35 https://archimer.ifremer.fr/doc/00830/94219/ |
op_rights |
info:eu-repo/semantics/openAccess restricted use |
op_doi |
https://doi.org/10.1017/eds.2022.35 |
container_title |
Environmental Data Science |
container_volume |
2 |
_version_ |
1782337303557963776 |