The prediction of two‐dimensional intelligent ocean temperature based on deep learning

Abstract

AbstractAn important data in intelligent ocean is the sea surface temperature (SST). Most of the previous works on SST prediction deal with independent spatial point, ignoring the spatial correlation of two‐dimensional intelligent ocean, which leads to instability of prediction accuracy. Therefore, in this paper, a deep learning model based on convolutional gated recurrent unit is proposed for the SST prediction of two‐dimensional intelligent ocean. Input and output of the proposed model are both spatiotemporal SST data, which means the model directly process spatiotemporal data. This method adds the spatial information of the intelligent ocean temperature data, thereby improving the accuracy. From the experiments, it turns out that the proposed model shows superior performance compared to another three prevailing deep learning models.