Wind–Wave Lag-Aware Deep Learning for Multi-Horizon Significant Wave Height Forecasting

Ocean wave evolution reflects delayed wind forcing, nonlinear wind–wave interactions, and wave persistence, which complicate multi-horizon significant wave height (SWH) forecasting. This study analyzes wind–wave lag relationships and wave memory characteristics in the South China Sea and the Bay of Bengal using ERA5 data from 2022 to 2024, leading to region-specific input windows of 12 h and 21 h. Based on these findings, SSA-STLSTM-LT integrates Wind–Wave Lag Attention, ST-LSTM, a Temporal Transformer, and spatial self-attention within a direct six-horizon forecasting framework. Feature ablation further identifies a nine-variable input configuration. Under a common ERA5-based evaluation protocol, the model achieved horizon-averaged RMSE values of 0.098 m and 0.065 m in Regions A and B, respectively. It obtained the lowest MAE and RMSE across all six horizons in Region A. In Region B, it achieved the lowest RMSE from 3 to 24 h, remained comparable to the best model at 48 h, and was less accurate than the LT-enhanced recurrent baselines at 72 h. Independent altimeter validation further showed favorable observational consistency relative to PredRNN-LT and TrajGRU-LT, with smaller differences in Region B. The results indicate strong but region- and horizon-dependent forecasting advantages.