LEARNING THE SPATIO-TEMPORAL RELATIONSHIP BETWEEN WIND AND SIGNIFICANT WAVE HEIGHT USING DEEP LEARNING
Ocean wave climate has a significant impact on near-shore and offshore human activities, and its characterisation 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 valua...
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ftinstagro:oai:HAL:hal-03825412v1 2024-09-15T18:23:18+00:00 LEARNING THE SPATIO-TEMPORAL RELATIONSHIP BETWEEN WIND AND SIGNIFICANT WAVE HEIGHT USING DEEP LEARNING Monbet, Valérie Obakrim, Said Ailliot, Pierre Raillard, Nicolas 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) SIMulation pARTiculaire de Modèles Stochastiques (SIMSMART) Inria Rennes – Bretagne Atlantique Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut de Recherche Mathématique de Rennes (IRMAR) 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)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes) Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER) Laboratoire de Mathématiques de Bretagne Atlantique (LMBA) Université de Bretagne Sud (UBS)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS) 2022-10-22 https://hal.science/hal-03825412 https://hal.science/hal-03825412/document https://hal.science/hal-03825412/file/Learning_relation_wind_waves.pdf en eng HAL CCSD hal-03825412 https://hal.science/hal-03825412 https://hal.science/hal-03825412/document https://hal.science/hal-03825412/file/Learning_relation_wind_waves.pdf info:eu-repo/semantics/OpenAccess https://hal.science/hal-03825412 2022 Wind fields Significant wave height Convolutional Neural Networks Long Short-Term Memory [MATH.MATH-ST]Mathematics [math]/Statistics [math.ST] info:eu-repo/semantics/preprint Preprints, Working Papers, . 2022 ftinstagro 2024-07-10T23:31:09Z Ocean wave climate has a significant impact on near-shore and offshore human activities, and its characterisation 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 spatio-temporal relationship between North Atlantic wind and significant wave height (H s) at an offshore location in the Bay of Biscay, using a two-stage deep learning model. The first step uses convolutional neural networks (CNNs) to extract the spatial features that contribute to H s. Then, long short-term memory (LSTM) is used to learn the long-term temporal dependencies between wind and waves. Report North Atlantic Portail HAL Institut Agro |
institution |
Open Polar |
collection |
Portail HAL Institut Agro |
op_collection_id |
ftinstagro |
language |
English |
topic |
Wind fields Significant wave height Convolutional Neural Networks Long Short-Term Memory [MATH.MATH-ST]Mathematics [math]/Statistics [math.ST] |
spellingShingle |
Wind fields Significant wave height Convolutional Neural Networks Long Short-Term Memory [MATH.MATH-ST]Mathematics [math]/Statistics [math.ST] Monbet, Valérie Obakrim, Said Ailliot, Pierre Raillard, Nicolas LEARNING THE SPATIO-TEMPORAL RELATIONSHIP BETWEEN WIND AND SIGNIFICANT WAVE HEIGHT USING DEEP LEARNING |
topic_facet |
Wind fields Significant wave height Convolutional Neural Networks Long Short-Term Memory [MATH.MATH-ST]Mathematics [math]/Statistics [math.ST] |
description |
Ocean wave climate has a significant impact on near-shore and offshore human activities, and its characterisation 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 spatio-temporal relationship between North Atlantic wind and significant wave height (H s) at an offshore location in the Bay of Biscay, using a two-stage deep learning model. The first step uses convolutional neural networks (CNNs) to extract the spatial features that contribute to H s. Then, long short-term memory (LSTM) 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) SIMulation pARTiculaire de Modèles Stochastiques (SIMSMART) Inria Rennes – Bretagne Atlantique Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut de Recherche Mathématique de Rennes (IRMAR) 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)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes) Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER) Laboratoire de Mathématiques de Bretagne Atlantique (LMBA) Université de Bretagne Sud (UBS)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS) |
format |
Report |
author |
Monbet, Valérie Obakrim, Said Ailliot, Pierre Raillard, Nicolas |
author_facet |
Monbet, Valérie Obakrim, Said Ailliot, Pierre Raillard, Nicolas |
author_sort |
Monbet, Valérie |
title |
LEARNING THE SPATIO-TEMPORAL RELATIONSHIP BETWEEN WIND AND SIGNIFICANT WAVE HEIGHT USING DEEP LEARNING |
title_short |
LEARNING THE SPATIO-TEMPORAL RELATIONSHIP BETWEEN WIND AND SIGNIFICANT WAVE HEIGHT USING DEEP LEARNING |
title_full |
LEARNING THE SPATIO-TEMPORAL RELATIONSHIP BETWEEN WIND AND SIGNIFICANT WAVE HEIGHT USING DEEP LEARNING |
title_fullStr |
LEARNING THE SPATIO-TEMPORAL RELATIONSHIP BETWEEN WIND AND SIGNIFICANT WAVE HEIGHT USING DEEP LEARNING |
title_full_unstemmed |
LEARNING THE SPATIO-TEMPORAL RELATIONSHIP BETWEEN WIND AND SIGNIFICANT WAVE HEIGHT USING DEEP LEARNING |
title_sort |
learning the spatio-temporal relationship between wind and significant wave height using deep learning |
publisher |
HAL CCSD |
publishDate |
2022 |
url |
https://hal.science/hal-03825412 https://hal.science/hal-03825412/document https://hal.science/hal-03825412/file/Learning_relation_wind_waves.pdf |
genre |
North Atlantic |
genre_facet |
North Atlantic |
op_source |
https://hal.science/hal-03825412 2022 |
op_relation |
hal-03825412 https://hal.science/hal-03825412 https://hal.science/hal-03825412/document https://hal.science/hal-03825412/file/Learning_relation_wind_waves.pdf |
op_rights |
info:eu-repo/semantics/OpenAccess |
_version_ |
1810463486477598720 |