中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 自动化研究所
  3. 中国科学院自动化研究所
  4. 多模态人工智能系统全国重点实验室
  5. 人工智能与机器学习
Dynamic graphs attention for ocean variable forecasting

文献类型：期刊论文

作者Wang, Junhao1; Sun, Zhengya2,3; Yuan, Chunxin1; Li, Wenhui4; Liu, An-An4; Wei, Zhiqiang1; Yin, Bo1
刊名ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
出版日期2024-07-01
卷号133页码:12
关键词Dynamic graphs Attention Ocean variable forecasting Neural network
ISSN号0952-1976
DOI10.1016/j.engappai.2024.108187
通讯作者Yin, Bo(ybfirst@ouc.edu.cn)
英文摘要Forecasting the ocean dynamics is a critical issue for a wide array of climate extremes and environmental crisis. The dynamic variations are traditionally approached by relying on numerical models with all the related physical processes identified beforehand. An efficient alternative forecasting approach is based on the datadriven models. Despite their potential ability in modeling spatio-temporal ocean data, they ignore the fact that the ocean variables in different spatial regions and time periods typically have ever changing influences on each other, thus cannot yield satisfactory prediction results. In this paper, we develop a novel attention based dynamic graph for the ocean variable forecasting problem, which captures both the spatial and temporal dependencies. Specifically, we employ joint self-attention to incorporate information from the spatial graph over the target region, and model the graph evolution across long-range time steps. The performance of the proposed prediction model has been examined in the Indian Ocean based on ocean grid data products datasets. Experimental results demonstrate that this model has significant forecasting capability within 12 months, compared with the numerical methods and the state-of-the-art spatio-temporal embedding baselines.
WOS关键词SEA-SURFACE TEMPERATURE ; PREDICTION ; MODEL ; ROMS
资助项目National Key Research and Development Program of China[2021YFF0704000] ; Joint Funds of the National Natural Science Foundation of China[2020JMRH0201] ; Key Research and Development Project of Shandong Province, China[U22A2068] ; [U23A20320]
WOS研究方向Automation & Control Systems ; Computer Science ; Engineering
语种英语
WOS记录号WOS:001221608800001
出版者PERGAMON-ELSEVIER SCIENCE LTD
资助机构National Key Research and Development Program of China ; Joint Funds of the National Natural Science Foundation of China ; Key Research and Development Project of Shandong Province, China
源URL[http://ir.ia.ac.cn/handle/173211/58327]  
专题精密感知与控制研究中心_人工智能与机器学习
通讯作者Yin, Bo
作者单位1.Ocean Univ China, Dept Comp Sci & Technol, Qingdao 266100, Shandong, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100190, Peoples R China
3.Chinese Acad Sci, Inst Automat, State Key Lab Multimodal Artificial Intelligence S, Beijing 100190, Peoples R China
4.Tianjin Univ, Sch Elect & Informat Engn, Tianjin 300072, Peoples R China
推荐引用方式
GB/T 7714		 Wang, Junhao,Sun, Zhengya,Yuan, Chunxin,et al. Dynamic graphs attention for ocean variable forecasting[J]. ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE,2024,133:12.
APA Wang, Junhao.,Sun, Zhengya.,Yuan, Chunxin.,Li, Wenhui.,Liu, An-An.,...&Yin, Bo.(2024).Dynamic graphs attention for ocean variable forecasting.ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE,133,12.
MLA Wang, Junhao,et al."Dynamic graphs attention for ocean variable forecasting".ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 133(2024):12.

入库方式： OAI收割

来源：自动化研究所

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。