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Pré-Publication, Document De Travail Année : 2022

LEARNING THE SPATIO-TEMPORAL RELATIONSHIP BETWEEN WIND AND SIGNIFICANT WAVE HEIGHT USING DEEP LEARNING

Résumé

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.
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Dates et versions

hal-03825412 , version 1 (22-10-2022)

Identifiants

  • HAL Id : hal-03825412 , version 1

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Valérie Monbet, Said Obakrim, Pierre Ailliot, Nicolas Raillard. LEARNING THE SPATIO-TEMPORAL RELATIONSHIP BETWEEN WIND AND SIGNIFICANT WAVE HEIGHT USING DEEP LEARNING. 2022. ⟨hal-03825412⟩
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