Time Series Prediction of Sea Surface Temperature Based on an Adaptive Graph Learning Neural Model

Tingting Wang, Zhuolin Li, Xiulin Geng, Baogang Jin and Lingyu Xu
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Tingting Wang: Department of Computer Engineering and Science, Shanghai University, Shanghai 200444, China
Zhuolin Li: Department of Computer Engineering and Science, Shanghai University, Shanghai 200444, China
Xiulin Geng: Department of Computer Engineering and Science, Shanghai University, Shanghai 200444, China
Baogang Jin: Beijing Institute of Applied Meteorology, Beijing 100029, China
Lingyu Xu: Department of Computer Engineering and Science, Shanghai University, Shanghai 200444, China

Future Internet, 2022, vol. 14, issue 6, 1-13

Abstract: The accurate prediction of sea surface temperature (SST) is the basis for our understanding of local and global climate characteristics. At present, the existing sea temperature prediction methods fail to take full advantage of the potential spatial dependence between variables. Among them, graph neural networks (GNNs) modeled on the relationships between variables can better deal with space–time dependency issues. However, most of the current graph neural networks are applied to data that already have a good graph structure, while in SST data, the dependency relationship between spatial points needs to be excavated rather than existing as prior knowledge. In order to predict SST more accurately and break through the bottleneck of existing SST prediction methods, we urgently need to develop an adaptive SST prediction method that is independent of predefined graph structures and can take full advantage of the real temporal and spatial correlations hidden indata sets. Therefore, this paper presents a graph neural network model designed specifically for space–time sequence prediction that can automatically learn the relationships between variables and model them. The model automatically extracts the dependencies between sea temperature multi-variates by embedding the nodes of the adaptive graph learning module, so that the fine-grained spatial correlations hidden in the sequence data can be accurately captured. Figure learning modules, graph convolution modules, and time convolution modules are integrated into a unified end-to-end framework for learning. Experiments were carried out on the Bohai Sea surface temperature data set and the South China Sea surface temperature data set, and the results show that the model presented in this paper is significantly better than other sea temperature model predictions in two remote-sensing sea temperature data sets and the surface temperature of the South China Sea is easier to predict than the surface temperature of the Bohai Sea.

Keywords: time series; deep learning; graph structure learning; spatial-temporal graph; prediction; sea surface temperature (SST) (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2022
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