A significant wave height forecast framework with end-to-end dynamic modeling and lag features length optimization

Ocean wave energy is attracting more and more attention from researchers on observation of its clean and sustainable properties. Significant wave height (SWH) is one of the key wave parameters, making accurately forecasting the SWH important for coastal/ocean engineers. In this paper, we propose a new framework for forecasting the SWH. The data were decomposed and reconstructed, and the lag features length of the input data were adaptively optimized using the Bayesian optimization (BO) algorithm. A new paradigm for end-to-end dynamic modeling (EEDM) forecast is then proposed, where data are modeled and forecasted separately for buoys at various geographical locations, with automated machine learning (AutoML) as the back-end modeling support for the paradigm. It was also trained and tested on nine buoys from NOAA National Data Buoy Center, which are located at sites with different water depths. The results show that the forecast framework has provided reliable forecasts. We also discussed the reasons for the buoy with the worst forecast in terms of model interpretability and data quality. Finally, we compared three deep learning models (simple recurrent network, long short-term memory and gate recurrent unit) and three machine learning models (principal component regression, support vector machine and K-nearest neighbor). The comparisons indicate that the AutoML turns out to be the best.