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个人信息Personal Information
副教授
博士生导师
硕士生导师
教师英文名称：Chia-Hung Wang
教师拼音名称：ChiaHung Wang
所在单位：计算机科学与数学学院
职务：副教授
学历：研究生(博士后)
办公地点：计算机科学与数学学院计算机科学与技术教研室
联系方式：校址：福建省福州市闽侯县上街镇学府南路69号福建理工大学 (邮编: 350118) 教师主页: http://faculty.fjut.edu.cn/wang_chiahung/zh_CN/index.htm ResearchGate Website: https://www.researchgate.net/profile/Chia-Hung_Wang2
学位：博士学位
在职信息：在职
主要任职：副教授
其他任职：研究生导师
毕业院校：(中国台湾)政治大学
学科：计算机科学与技术
电子信息工程
交通运输
信息与计算科学
数据科学与大数据技术
论文成果
当前位置： 王嘉宏博士（Chia-Hung Wang, Ph.D.）中文主页 >> 科学研究 >> 论文成果Significant wave height prediction at multiple sites using sequence decomposition and dynamic spatiotemporal graph neural networks
点击次数：
影响因子：5.5
DOI码：10.1016/j.oceaneng.2025.122548
发表刊物：Ocean Engineering
关键字：Artificial intelligence; Marine energy; Significant wave height forecasting; Sequence decomposition; Graph neural network; Spatiotemporal dependency; Extreme wave events
摘要：With the continuous development of artificial intelligence technology, accurate prediction of significant wave height (SWH) has become increasingly important in the application of marine energy and renewable energy systems, especially in marine energy development, energy network optimization and response to extreme weather events. This study proposes a sequence decomposition dynamic spatiotemporal graph neural network (DSTGNN) prediction model to improve the prediction performance of significant wave height. The model decomposes the data into trend and seasonal components, uses frequency domain multi-layer perceptron to capture the temporal dynamic characteristics, and combines adaptive dynamic graph neural network to effectively model the complex spatial correlation between multiple buoy sites. Experimental results show that the DSTGNN model significantly outperforms traditional methods on data sets in the southeastern United States and the western Atlantic Ocean, the Gulf of Mexico and the Caribbean Sea, the northeastern United States, the coastal Pacific Ocean of the United States, and the Hawaiian Islands. In the prediction task of the next 6 hours, its MAPE values reach 0.1228, 0.2368, 0.3094, 0.2576 and 0.0798 respectively. This model can not only accurately capture the temporal and spatial dependence characteristics of wave data, but also provide higher accuracy in the prediction of extreme wave events, providing strong support for the development and prediction of marine energy and having significant energy application value.
备注：Anyone clicking on this link before October 17, 2025 will be taken directly to the final version of our article on ScienceDirect, which you are welcome to read or download. No sign up, registration or fees are required. Our personalized Share Link: https://authors.elsevier.com/a/1lge46nh7CR5c
论文类型：期刊论文
学科门类：工学
文献类型：J
卷号：341
期号：2
页面范围：122548
ISSN号：0029-8018
是否译文：否
发表时间：2025-08-28
收录刊物：SCI
发布期刊链接：https://doi.org/10.1016/j.oceaneng.2025.122548
第一作者：林持旺
合写作者：谢永峰
通讯作者：王嘉宏