Spatio-temporal predictions of SST time series in China's offshore waters using a regional convolution long short-term memory (RC-LSTM) network. Issue 9 (2nd May 2020)

• Record Type: Journal Article
• Authors: Xu, Lingyu; Li, Qin; Yu, Jie; Wang, Lei; Xie, Jiang; Shi, Suixiang
• Is Part Of: International journal of remote sensing. Volume 41:Issue 9(2020)
• Page Start: 3368
• Page End: 3389
• Publication Date: 2020-05-02
• Subjects: Remote sensing -- Periodicals; Télédétection -- Périodiques; 621.3678
• DOI: 10.1080/01431161.2019.1701724
• Languages: English
• ISSNs: 0143-1161

Abstract

ABSTRACT: Sea surface temperature (SST) research is the basis of our understanding of local and global climate characteristics. Significant effort has been focused on the acquisition of accurate prediction results. Although a large amount of SST data is available due to the development of storage media, there are limited studies on the prediction of future SST values in a spatiotemporal manner. Most previous studies ignore the regional distribution information in natural geographical location, which leads to the loss of regional information and hinders further regional analysis. In this work, we have addressed this issue by presenting Regional Convolution long short-term memory (RC-LSTM) model with spatio-temporal information processing capability. To the best of our knowledge, this is the first time that SST time series has been used for region prediction based on deep learning. Daily SST from January 2000 to December 2012 from China's offshore waters is adopted to conduct experiments. The local correlation and global coherence of each time series are extracted by the multi-layer neural network in the model. The structure of the RC-LSTM model includes a convolutional layer with region information extraction capabilities, and a long short-term memory (LSTM) layer. The RC-LSTM model performs well over both the South China Sea and the East China Sea, as determined by the root mean squared error (RMSE) and the accuracy (Acc). The experimental results show that our model is more accurate than traditional prediction models and has higher generalization ability, and is therefore more practical.