Multiple Granularity Spatiotemporal Network for Sea Surface Temperature Prediction

Abstract

Sea surface temperature (SST) prediction has an important practical value in marine disaster prevention and mitigation. Most current methods only use the temporal correlation of SST during prediction, but the spatial correlation is not considered, resulting in low prediction accuracies. In addition, the changing trend of SST as reflected by the single granularity feature is unreliable, and the degrees of dependence between historical SST and future SST tend to vary. In order to overcome these issues, the multiple granularity spatiotemporal network (MGSN) is proposed for SST prediction. The proposed method consists of three parts. First, a multibranch network structure is constructed to extract different temporal features of different granularities. Second, a temporal dependence representation module is developed to represent the different degrees of dependence between historical SST and predicted SST in the temporal dimension. Third, the spatiotemporal fusion prediction module is used to achieve a spatiotemporal prediction of the SST and fuse the prediction results of different granular features. Comparative experiments have been conducted. The experimental results show that the root-mean-square error (RMSE) of the proposed method is reduced by 0.1360, 0.1608, and 0.1448 compared with the RMSE of convolutional LSTM (ConvLSTM), when predicting SST for the next one day, three days, and seven days, respectively. Our method has strong spatiotemporal feature modeling capabilities and is suitable for regional SST prediction.