Hybrid framework of deep extreme learning machine (DELM) based on sparrow search algorithm for non-stationary wave prediction.

Accurate and trustworthy wave height forecast is essential for the efficient exploitation of wave energy. However, the non-linearity and non-stationarity of waves present a challenge task when using the conventional statistical models in the ocean wave forecasting. Aiming at these problems, this paper proposes a short-term wave height prediction method based on the Variational Mode Decomposition (VMD), Deep Extreme Learning Machine (DELM) and Sparrow Search Algorithm (SSA). Specifically, the original wave height data is initially decomposed into numerous sub-series from high to low frequency by VMD technique. Subsequently, the SSA method is employed to optimize the DELM parameters to enhance the functionality of the basic DELM model. Three wave height datasets located in the North Pacific Ocean are employed as cases in this paper. In comparison with other benchmark models, the RMSE parameters of VMD-SSA-DELM model are reduced by 35.59%–73.33% even at 10-h lead time by analyzing different forecast durations, which demonstrate the proposed model's superiority in short-term wave height prediction. • A novel hybrid VMD-SSA-DELM approach is proposed for significant wave height prediction. • The VMD algorithm is employed for preprocessing, and the original sequence is decomposed into multiple sub-modes. • The validity and accuracy of the model are verified by the measured data in three regions of the North Pacific Ocean. • Through comparison with other benchmark models, the impacts of wave non-stationary properties and advance forecast duration on forecast performance are examined. [ABSTRACT FROM AUTHOR]