Sea surface temperature prediction method based on an IWOA optimized Res-BiGRU deep learning model

Sea surface temperature (SST) is one of the key elements in the study of marine environment and climate change. The existing SST prediction algorithm based on deep learning has the problems of time-consuming parameter adjustment and low prediction accuracy. Based on this, a SST prediction algorithm based on improved whale optimization (IWOA) residual bidirectional gated recurrent neural network (Res-BiGRU) is proposed. The two-dimensional monthly average SST spatial distribution and one-dimensional SST time series data in the East China Sea are selected to verify the feasibility and effectiveness of the algorithm. For the two-dimensional monthly average SST spatial distribution data, the Res-BiGRU neural network is used to extract the spatial and temporal features of the SST sequence signal, and the time and space attention mechanism is introduced in the decoding stage to adjust the weight distribution of the extracted features. Secondly, the IWOA algorithm is used to optimize the training parameters to improve the efficiency of training parameter adjustment and the accuracy of Res-BiGRU model. Aiming at the one-dimensional daily average SST time series data, the ensemble empirical mode decomposition (EEMD) method is introduced to preprocess the SST sequence signal, and then the IWOA-Res-BiGRU is used for feature extraction and SST prediction. The experimental results show that the proposed Res-BiGRU algorithm based on IWOA optimization can predict the mean square error of two-dimensional monthly mean SST and one-dimensional daily mean SST by 0.35 ℃ and 0.09 ℃, respectively. The prediction results are better than other models and can become one of the important tools for SST prediction.