Prediction of Significant Wave Height Based on FFT-MaxVIT
Accurate prediction of significant wave height (SWH) is critical for marine hazard warning and coastal engineering, yet its stochastic nature impedes high-precision forecasting. This study proposes a hybrid FFT-MaxVIT model that integrates fast Fourier transform (FFT) with multi-head axial attention. The FFT extracts dominant frequency components from X-band radar images while suppressing noise; convolutional layers capture local features, and block and grid attention mechanisms efficiently extract global features under small-sample conditions. Pruning and Bayesian optimization are employed for hyperparameter tuning. Field data were collected from a wave rider buoy and X-band radar deployed near an island in Dalian from November 4–10, 2023. The buoy provided point measurements of SWH every 200 s (2225 groups), while radar acquired images every 5 s (60252 images). Training used 11254 samples (SWH 50–360 cm) from November 5–9, and testing used 2256 samples (SWH 90–200 cm) from November 9–10. Comparative experiments against LSTM, ResNet, and ViT models demonstrate that FFT-MaxVIT achieves an eMAPE of 4.97%, eMAE of 7.07 cm, eRMSE of 8.78 cm, and R² of 0.94, significantly outperforming all baselines. The results confirm that frequency-domain preprocessing combined with efficient attention mechanisms substantially improves SWH prediction accuracy under limited data.