Learning the spatiotemporal relationship between wind and significant wave height using deep learning
International audience 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. Nume...
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ftunibretagnesud:oai:HAL:hal-04204018v1 2024-05-19T07:44:59+00:00 Learning the spatiotemporal relationship between wind and significant wave height using deep learning Obakrim, Said Monbet, Valerie Raillard, Nicolas Ailliot, Pierre Institut de Recherche Mathématique de Rennes (IRMAR) Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes) Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-École normale supérieure - Rennes (ENS Rennes)-Université de Rennes 2 (UR2)-Centre National de la Recherche Scientifique (CNRS)-Institut Agro Rennes Angers Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro) Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER) Unité Recherches et Développements Technologiques (RDT) Laboratoire de Mathématiques de Bretagne Atlantique (LMBA) Université de Bretagne Sud (UBS)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS) 2023-02-15 https://hal.science/hal-04204018 https://doi.org/10.1017/eds.2022.35 en eng HAL CCSD Cambridge University Press info:eu-repo/semantics/altIdentifier/arxiv/2205.13325 info:eu-repo/semantics/altIdentifier/doi/10.1017/eds.2022.35 hal-04204018 https://hal.science/hal-04204018 ARXIV: 2205.13325 doi:10.1017/eds.2022.35 EISSN: 2634-4602 Environmental Data Science https://hal.science/hal-04204018 Environmental Data Science, 2023, 2 (E5), 8p. ⟨10.1017/eds.2022.35⟩ [SDU]Sciences of the Universe [physics] info:eu-repo/semantics/article Journal articles 2023 ftunibretagnesud https://doi.org/10.1017/eds.2022.35 2024-05-02T00:09:41Z International audience 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 Université de Bretagne Sud: HAL Environmental Data Science 2 |
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Université de Bretagne Sud: HAL |
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English |
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[SDU]Sciences of the Universe [physics] |
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[SDU]Sciences of the Universe [physics] Obakrim, Said Monbet, Valerie Raillard, Nicolas Ailliot, Pierre Learning the spatiotemporal relationship between wind and significant wave height using deep learning |
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[SDU]Sciences of the Universe [physics] |
description |
International audience 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. |
author2 |
Institut de Recherche Mathématique de Rennes (IRMAR) Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes) Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-École normale supérieure - Rennes (ENS Rennes)-Université de Rennes 2 (UR2)-Centre National de la Recherche Scientifique (CNRS)-Institut Agro Rennes Angers Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro) Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER) Unité Recherches et Développements Technologiques (RDT) Laboratoire de Mathématiques de Bretagne Atlantique (LMBA) Université de Bretagne Sud (UBS)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS) |
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 |
HAL CCSD |
publishDate |
2023 |
url |
https://hal.science/hal-04204018 https://doi.org/10.1017/eds.2022.35 |
genre |
North Atlantic |
genre_facet |
North Atlantic |
op_source |
EISSN: 2634-4602 Environmental Data Science https://hal.science/hal-04204018 Environmental Data Science, 2023, 2 (E5), 8p. ⟨10.1017/eds.2022.35⟩ |
op_relation |
info:eu-repo/semantics/altIdentifier/arxiv/2205.13325 info:eu-repo/semantics/altIdentifier/doi/10.1017/eds.2022.35 hal-04204018 https://hal.science/hal-04204018 ARXIV: 2205.13325 doi:10.1017/eds.2022.35 |
op_doi |
https://doi.org/10.1017/eds.2022.35 |
container_title |
Environmental Data Science |
container_volume |
2 |
_version_ |
1799484901449269248 |