Regional forecasting of significant wave height and mean wave period using EOF-EEMD-SCINet hybrid model

Abstract

Most studies only focus on single-point wave forecasting, while regional wave forecasting has more important significance for ocean engineering construction, navigation safety and disaster warning. To solve the problems of high computational cost and poor performance of regional wave forecasting models, a novel hybrid model for regional significant wave height (SWH) and mean wave period (MWP) forecasting, called EOF-EEMD-SCINet, is proposed by combining empirical orthogonal functions (EOF) analysis, ensemble empirical mode decomposition (EEMD) and sample convolution and interaction network (SCINet). After determining the optimal input steps, the number of intrinsic mode functions (IMFs) and the decomposition method, the forecasting performance of the model for SWH and MWP with lead times of 24, 48 and 72 h is evaluated based on SWH, MWP, wind speed (WSPD), significant height of first swell partition (SWH1) and significant height of wind waves (SHWW) in the South China Sea (SCS). The results show that the proposed model can not only accurately forecast the changes in SWH and MWP with time, but also has a high forecasting precision for regional waves, which is superior to other models. In addition, the increase of wave lead time has less negative effect on the forecasting performance of the proposed model compared to other models. As the lead time changes from 24 h to 72 h, the mean absolute error (MAE) of SWH increases by less than 6 cm, and the MAE of MWP changes around 0.04 s. Besides, EOF-EEMD-SCINet has enormous advantage in terms of efficiency. EOF-EEMD-SCINet is a model that can simultaneously forecast regional SWH and MWP with high precision.