Learning the spatio-temporal relationship between wind and significant wave height using deep learning

TL;DR

This paper presents a deep learning approach combining CNNs and LSTMs to model the spatio-temporal relationship between wind and significant wave height in the North Atlantic, aiding ocean activity planning.

Contribution

It introduces a novel two-stage deep learning model that captures both spatial features and long-term temporal dependencies between wind and wave height.

Findings

Effective modeling of wind-wave relationship demonstrated

Deep learning outperforms traditional statistical methods

Potential for improved ocean activity planning

Abstract

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.