A hybrid sea surface temperature predicting method based on deep learning

Sea surface temperature (SST) is an important parameter of ocean hydrology, and accurate prediction of SST is of great significance for ocean economic development and extreme weather prevention. For the characteristics of SST series with multiple noises, variational model decomposition (VMD) is used to pre-process the SST series and reduce the influence of noise on the prediction results. Furthermore, the convolutional neural network (CNN) is combined with the long short-term memory network (LSTM) extracting both spatial and temporal features in SST sequences to improve the prediction accuracy. Finally, a SST prediction model based on deep learning with incorporating the denoising module is proposed in this paper. The SST of China's East China Sea waters is selected for empirical study. Through comparison and analysis with the baseline models and existing models, it is proved that the model in this paper not only improves the SST prediction accuracy significantly, but also has better robustness.