利用基于残差学习的纯 CNN 方法和注意机制进行时空近海流场预测

IF 2.9 4区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Applied Artificial Intelligence Pub Date : 2024-03-04 DOI:10.1080/08839514.2024.2323827

Zeguo Zhang, Jianchuan Yin

引用次数: 0

摘要

时空海流预报是海洋工程和海洋科学探索不可或缺的工具，例如有助于养护和保护海洋生态系统、规划航运和海洋生物资源。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Spatial-temporal Offshore Current Field Forecasting Using Residual-learning Based Purely CNN Methodology with Attention Mechanism

Spatial-temporal current forecasting is indispensable for ocean engineering and marine science exploration, for instance aiding in the conservation and protection of marine ecosystems, planning shi...

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

Applied Artificial Intelligence 工程技术-工程：电子与电气

CiteScore

5.20

自引率

3.60%

发文量

106

审稿时长

6 months

期刊介绍： Applied Artificial Intelligence addresses concerns in applied research and applications of artificial intelligence (AI). The journal also acts as a medium for exchanging ideas and thoughts about impacts of AI research. Articles highlight advances in uses of AI systems for solving tasks in management, industry, engineering, administration, and education; evaluations of existing AI systems and tools, emphasizing comparative studies and user experiences; and the economic, social, and cultural impacts of AI. Papers on key applications, highlighting methods, time schedules, person-months needed, and other relevant material are welcome.