MPST: effective significant wave height prediction method based on multiscale physical space-time-frequency fusion

Abstract Accurate prediction of significant wave height is crucial for maritime safety, offshore engineering, and disaster mitigation. Recent advancements in deep learning have improved wave height prediction by utilizing historical data. However, ocean waves exhibit highly complex and distinct dynamic characteristics across various temporal and spatial scales, resulting in intricate nonlinear couplings between spatial and frequency components. Since conventional methods primarily rely on feature extraction in the space-time domains, they cannot capture multiscale physical variations in wave dynamics accurately, ultimately limiting predictive accuracy. To address this limitation, we propose an effective wave height prediction method based on multiscale physical space-time-frequency (PSTF) fusion (MPST). The proposed method integrates two key modules: the multiscale feature extraction (MSFE) module, which utilizes atrous spatial pyramid pooling to enhance local-global wave characteristic extraction, and the PSTF information fusion module, which combines a Fourier neural operator and a Transformer to fuse frequency and spatial features dynamically. A gated fusion mechanism further optimizes feature weighting, improving accuracy and robustness. Experimental results on the ERA5 dataset show that MPST reduces overall prediction error by 7.43% compared to state-of-the-art methods. Notably, in high-wave regions (above 2, 4, and 8 m), prediction accuracy improves by 5.86%, 3.46%, and 8.84%, respectively. These findings highlight better stability and adaptability of the MPST in complex ocean environments. Thus, MPST offers a robust solution for precise wave height prediction and supports maritime safety and sustainable ocean engineering.