2024
DOI: 10.3390/rs16224126
|View full text |Cite
|
Sign up to set email alerts
|

Sea Surface Temperature Prediction Using ConvLSTM-Based Model with Deformable Attention

Benyun Shi,
Conghui Ge,
Hongwang Lin
et al.

Abstract: Sea surface temperature (SST) prediction has received increasing attention in recent years due to its paramount importance in the various fields of oceanography. Existing studies have shown that neural networks are particularly effective in making accurate SST predictions by efficiently capturing spatiotemporal dependencies in SST data. Among various models, the ConvLSTM framework is notably prominent. This model skillfully combines convolutional neural networks (CNNs) with recurrent neural networks (RNNs), en… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

0
0
0

Publication Types

Select...

Relationship

0
0

Authors

Journals

citations
Cited by 0 publications
references
References 32 publications
0
0
0
Order By: Relevance

No citations

Set email alert for when this publication receives citations?