Sea Surface Temperature Prediction Method Based on Deep Generative Adversarial Network
Jia Wang, Gang Zheng, Jiali Yu, Jinliang Shao, Yinfei Zhou · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · 2024
Sea surface temperature (SST) prediction plays an important role in ocean-related fields. Therefore, it is increasingly important to be able to make more accurate prediction of sea surface temperature. In this paper, we develop a Deep generative adversarial network (DGAN) for generating future maps of sea surface temperatures, providing a visual method of predicting sea surface temperatures. Our DGAN model consists of a generator and a discriminator. The generator is designed to produce more realistic maps of future sea surface temperatures, which uses multiple composite layers to capture the changes of sea surface temperatures and generates clear maps of future sea surface temperatures. The discriminator uses the structure of patchGAN to obtain more SST features, and distinguishes between real and generated SST maps. In addition, we improve the loss function and perform convergence analysis, and then obtain that minimizing the loss function is equivalent to minimizing Pearson$\chi 2$divergence, and the relevant explanations are carried out through experiments. The generator and discriminator are training adversarially during the training stage, eventually reaching a relatively balanced state, and the DGAN is able to produce more reliable visual predictions. Finally, the effectiveness of DGAN in the prediction of sea surface temperature is verified experimentally, and it is compared with the generative model-DL model and the LSTM-GAN model.