A Hybrid Deep Learning Model Based on FFT-STL Decomposition for Ocean Wave Height Prediction - Yelian Sun; Longkun Yu; Dandan Zhu (Applied Sciences)
Accurate prediction of the height of ocean waves is critical to ensuring maritime safety, optimizing offshore operations, and mitigating coastal hazards. To improve the accuracy of ocean wave height prediction, we developed a hybrid model that integrates decomposition and deep learning. The approach combines Fourier transform, seasonal and trend decomposition using Loess, and various deep learning models, which can more accurately capture the periodicity, trends, and random fluctuations. The trend, seasonality, and residual components are predicted using the LSTM model, SARIMAX, and 1D-CNN, respectively. The mean square error of the model prediction was calculated to be 0.0087 and the root mean square error was 0.0935. The results show that the hybrid model outperforms the other methods compared in our experiments. This model can accurately predict ocean wave heights and provides a reference for predicting time-series data with seasonal fluctuations.