Wengeng Shen Zongquan Ying Yiming Zhao Xuegang Wang ^*

• CCCC Fourth Harbor Engineering Institute Co., Ltd, Guangzhou, China

A novel significant wave height prediction method for monsoon regions is proposed, utilizing the VMD-CNN-BiLSTM model to enhance prediction accuracy under complex meteorological conditions. Traditional numerical models exhibit limitations in managing extreme marine conditions and fail to fully integrate wind field information. Meanwhile, existing machine learning models demonstrate insufficient generalization and robustness for long-term predictions. To address these shortcomings, the predictive approach combines Variational Mode Decomposition (VMD) with a hybrid deep learning model (CNN-BiLSTM). VMD is employed to decompose the original wave height sequence and extract key features, while CNN captures the spatial features of wind field and wave height data. BiLSTM, in turn, models the temporal dependencies. Experimental results reveal that the VMD-CNN-BiLSTM model provides substantial advantages in prediction performance across all seasons, including the entire year. Compared to traditional models, the proposed method demonstrates significantly reduced Mean Absolute Error (MAE) and Root Mean Square Error (RMSE), alongside an improved coefficient of determination (R² ). These findings confirm the effectiveness and reliability of the method under complex meteorological conditions such as monsoons and typhoons.