Accurate prediction of SWH in the ocean is critical to the development of ocean energy technologies. However, existing forecasting models have limitations in accuracy and reliability, which directly affect the efficiency of ocean energy utilization. To address this problem, this paper proposes a new ocean SWH prediction model, RIME-CNN-BiLSTM, for predicting 1, 6, 12, and 24 h SWH at three buoy observing stations in the Gulf of Mexico. The model combines the RIME algorithm, CNN, and BiLSTM to comprehensively capture multiple parameters that affect wave heights, such as sea breeze, waves, barometric pressure, and temperature. The parameters of the CNN-BiLSTM model are optimized by the RIME algorithm, which significantly improves the prediction accuracy and generalization ability. Compared with the traditional model, RIME-CNN-BiLSTM captures the nonlinear features more accurately, has better prediction ability for outliers, and shows stronger generalization performance in different sea areas of the Gulf of Mexico. In terms of predicting the wave height in the next 24 h, the improvements are 7.065%, 3.646% and 5.186%, respectively. The results show that the RIME-CNN-BiLSTM model performs well in all prediction time periods, significantly outperforms other benchmark models, and is able to accurately capture nonlinear features and effectively predict outliers.
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