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

• Published in: Environmental Data Science
• Main Authors: Said Obakrim, Valérie Monbet, Nicolas Raillard, Pierre Ailliot
• Format: Article in Journal/Newspaper
• Language: English
• Published: Cambridge University Press 2023
• Subjects: Convolutional neural networks; long short-term memory; significant wave height; wind fields; Environmental sciences; GE1-350; Electronic computers. Computer science; QA75.5-76.95; North Atlantic
• Online Access: https://doi.org/10.1017/eds.2022.35; https://doaj.org/article/344e98ef7c764d0689e4b31b1f25274c

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.