Learning the spatio-temporal relationship between wind and significant wave height using deep learning
This work addresses the need for efficient wave data prediction for engineers designing ocean structures, but it appears incremental as it applies established deep learning techniques to a specific domain.
The paper tackles the problem of predicting significant wave height from wind data using a two-stage deep learning model combining CNNs and LSTMs, aiming to provide computationally efficient alternatives to expensive numerical models for ocean wave parameter estimation.
Ocean wave climate has a significant impact on near-shore and off-shore 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 (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 (CNNs) to extract the spatial features that contribute to Hs. Then, long short-term memory (LSTM) is used to learn the long-term temporal dependencies between wind and waves.