STAGRU: Ocean Surface Current Spatio-Temporal Prediction Based on Deep Learning

Chen, Ping; Chi, Meng-Ya

doi:10.1109/cisai54367.2021.00101

2021 International Conference on Computer Information Science and Artificial Intelligence (CISAI) 2021

DOI: 10.1109/cisai54367.2021.00101

|View full text |Cite

STAGRU: Ocean Surface Current Spatio-Temporal Prediction Based on Deep Learning

Ping Chen

Meng-Ya Chi

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2022

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Ocean Current Prediction Using the Weighted Pure Attention Mechanism

Liu

Yang

Liu

et al. 2022

JMSE

View full text Add to dashboard Cite

Ocean current (OC) prediction plays an important role for carrying out ocean-related activities. There are plenty of studies for OC prediction with deep learning to pursue better prediction performance, and the attention mechanism was widely used for these studies. However, the attention mechanism was usually combined with deep learning models rather than purely used to predict OC, or, if it was purely used, did not further optimize the attention weight. Therefore, a deep learning model based on weighted pure attention mechanism is proposed in this paper. This model uses the pure attention mechanism, introduces a weight parameter for the generated attention weight, and moves more attentions from other elements to the key elements based on weight parameter setting. To our knowledge, it is the first attempt to use the weighted pure attention mechanism to improve the OC prediction performance, and it is an innovation for OC prediction. The experiment results indicate that the proposed model can fully take advantage of the strengths from the pure attention mechanism; it can further optimize the pure attention mechanism and significantly improve the prediction performance, and is reliable for OC prediction with high performance for a wide time range and large spatial scope.

show abstract

Ocean Current Prediction Using the Weighted Pure Attention Mechanism

Liu

Yang

Liu

et al. 2022

JMSE

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

STAGRU: Ocean Surface Current Spatio-Temporal Prediction Based on Deep Learning

Cited by 1 publication

References 8 publications

Ocean Current Prediction Using the Weighted Pure Attention Mechanism

Ocean Current Prediction Using the Weighted Pure Attention Mechanism

Contact Info

Product

Resources

About