Abstract: The significant wave height forecast is crucial for both human marine activities and marine engineering. The artificial neural network has been widely used in significant wave height prediction and achieved good results. However, as a shallow network architecture, it has limited expressive ability, making the forecast accuracy fluctuate in different regions. Therefore, to improve the overall forecast accuracy of the significant wave height, this paper proposes a deep learning model of significant wave height forecasting by integrating stacked autoencoders (SAE) and XGBoost. First, the powerful feature representation capabilities of the SAE algorithm are used to process ocean wave data to realize the extended dimension expression of the data. Secondly, the deep feature expression of SAE is used as the input of the XGBoost algorithm to predict effective wave heights. This paper focuses on the significant wave height prediction method and uses the measured wave data of Buoy 2 in the central Taiwan Strait in 2017. The experimental results show that our approach is superior to existing methods in terms of deterministic coefficient (R^2) and mean square error (MSE).

Key words: significant wave height, stacked autoencoders, XGBoost, deep learning