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

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
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Abstract

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...
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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
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。
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来源期刊
Applied Artificial Intelligence
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.
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