Disentangled Seasonal-Trend representation of improved CEEMD-GRU joint model with entropy-driven reconstruction to forecast significant wave height

Abstract

In recent years, wave energy has gained popularity among marine researchers for its sustainability, cleanliness, high energy density and wide distribution. As one of the most important parameters of wave energy, Significant Wave Height (SWH) is difficult to predict accurately due to complex sea conditions and the ubiquitous chaotic phenomena in nature. The efficient use of wave energy cannot be achieved without accurate analysis and prediction of SWH. In this study, a novel DST-CEEMD-SampEn-GRU joint model is proposed, which combines Disentangled Seasonal-Trend (DST) to reduce the computational time of the Complementary Ensemble Empirical Mode Decomposition (CEEMD). This is combined with SampEn-based reconstruction to avoid the tremendous computational effort associated with IMF components of CEEMD. The final prediction is made using the Gate Recurrent Unit (GRU). In this paper, we demonstrate the effectiveness of CEEMD, SampEn and DST in improving accuracy through forward ablation experiments respectively. Finally, the proposed model is validated with multi-single machine learning models for muti-hour ahead SWH forecasting. The experimental results demonstrate that DST-CEEMD-SampEn-GRU is statistically superior to all the other methods.