文献类型：期刊文献

英文题名：Attention Adaptive Temporal Graph Convolutional Network for Long-Term Seasonal Sea Surface Temperature Forecasting

作者：Xiao, Ling[1];Yang, Peihao[2];Wang, Yuxue[1];Li, Sheng[1];Chen, Baoqin[1]

机构：[1]Guangdong Ocean Univ, Sch Math & Comp, Zhanjiang 524088, Peoples R China;[2]Guangdong Ocean Univ, Coll Ocean & Meteorol, Zhanjiang 524088, Peoples R China

年份：2024

卷号：17

起止页码：19003

外文期刊名：IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING

收录：SCI-EXPANDED(收录号：WOS:001342550900001)、、EI(收录号：20244117174946)、Scopus(收录号：2-s2.0-85205927601)、WOS

基金：This work was supported by the National Science Fund of China under Grant 12101138.

语种：英文

外文关键词：Forecasting; Biological system modeling; Predictive models; Meteorology; Market research; Fluctuations; Data models; Adaptive systems; Graph convolutional networks; Attention mechanisms; Attention mechanism pruning; dynamic graph; long-term seasonal forecasting; sea surface temperature (SST)

外文摘要：Sea surface temperature (SST) plays a crucial role in the global meteorological system, particularly as long-term seasonal variations are significant for analyzing SST anomalies and supporting long-term climate decision-making. Current forecasting methods are primarily focused on short-term or fine-grained predictions and often fail to effectively capture long-term seasonal trends. To address this, we introduce a novel attention adaptive temporal graph convolutional network (AA-TGCN) specifically designed for long-term seasonal SST forecasting. Unlike traditional adaptive graph convolutional networks, the AA-TGCN incorporates an attention mechanism to capture internode correlations and utilizes a pruning strategy from SGAT to eliminate noisy connections, thereby improving the model's inductive learning capabilities. Moreover, the network employs a TCN-like architecture to expand its receptive field, enhancing its ability to grasp long-term trends, and employs differential embedding to further refine the prediction accuracy of seasonal fluctuations. Practical applications in the Bohai Sea and parts of the South China Sea demonstrate that AA-TGCN outperforms existing technologies on multiple scales, particularly achieving significant improvements in the South China Sea regions.