文献类型：期刊文献

英文题名：Marine heatwave prediction method for the South China Sea

作者：Yang, Peihao[1];Fan, Lingli[1];Chang, Shujie[1,3];Ye, Guodong[1,2]

机构：[1]Guangdong Ocean Univ, Coll Ocean & Meteorol, Zhanjiang 524088, Peoples R China;[2]Guangdong Ocean Univ, Fac Math & Comp Sci, Zhanjiang 524088, Peoples R China;[3]Univ Leeds, Sch Earth & Environm, Leeds LS2 9JT, England

年份：2025

卷号：252

外文期刊名：JOURNAL OF MARINE SYSTEMS

收录：SCI-EXPANDED(收录号：WOS:001612944300001)、、EI(收录号：20254419437257)、Scopus(收录号：2-s2.0-105020377965)、WOS

基金：This study was supported in part by the Innovation Team Project of the General University in the Guangdong Province, China (Grant number 2024KCXTD042) .

语种：英文

外文关键词：Marine heatwave; South China Sea; Graph neural network; Long-term prediction; Multivariate multi-granularity

外文摘要：Marine heat waves (MHWs) are prolonged anomalous sea temperature phenomena that affect marine ecosystems and their accurate prediction is of significance. In this study, a multivariate multi-granularity spatiotemporal graph neural network (MMS-GNN) based on a transformer is proposed. First, time series are sorted by period. The transformer is then used to capture the long-term dependency of various temporal granularities. Second, the entire marine region is divided into several subregions. The spatial dependency between different locations is then established by studying the changing trends between neighboring nodes. Third, a GNN is combined with a temporal convolutional network and employed to predict the collected information. Finally, experiments are conducted in the South China Sea using 40 years of observational data. Test results demonstrate that the proposed MMS-GNN can outperform other methods, meaning high effectiveness and flexibility of MMS-GNN for long-term MHW prediction.