Forecasting of Sea-Surface Wind Speed Using Deep-Learning Method Based on Multidimensional Frequency-Domain Feature Fusion

Sea-surface wind is a vital meteorological element in marine activities and climate research. This study proposed the spectral attention enhanced multidimensional feature fusion convolutional long short-term memory (LSTM) network (SAMFF-ConvLSTM), a novel approach for sea-surface wind-speed prediction that emphasizes the temporal characteristics of data samples. The model incorporates the Fourier transform to extract time- and frequency-domain features from wave and wind variables. For the 12 h prediction, the SAMFF-ConvLSTM achieved a correlation coefficient of 0.960 and a root mean square error (RMSE) of 1.350 m/s, implying a high prediction accuracy. For the 24 h prediction, the RMSE of the SAMFF-ConvLSTM was reduced by 38.11%, 14.26%, and 13.36% compared with those of the convolutional neural network, gated recurrent units, and convolutional LSTM (ConvLSTM), respectively. These results confirm the superior reliability and accuracy of the SAMFF-ConvLSTM over traditional models in theoretical and practical applications.