Name
Abstract | ||
|---|---|---|
Sea surface temperature (SST) prediction has raised considerable attention in various ocean-related fields. However, these methods were only limited to the time-sequence prediction of some isolated points, and their spatial linkage was not considered. Furthermore, these studies only predict the temperature of sea surface, but the subsurface temperature in the inner ocean is much more important. In this letter, we propose a model of multilayer convolutional long- and short-term memory (M-convLSTM) to predict 3-D ocean temperature, comprising convolutional neural networks (CNNs), long- and short-term memory (LSTM), and multiple layer stacking to consider the horizontal and vertical temperature variations from sea surface to subsurface to be about 2000 m below. Global marine environment observation data (ARGO) are used to conduct the prediction of 3-D ocean temperature in this letter, and the results demonstrate the overall good accuracy of forecast and ARGO data. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/LGRS.2019.2947170 | IEEE Geoscience and Remote Sensing Letters |
Keywords | DocType | Volume |
Ocean temperature,Logic gates,Feature extraction,Training,Sea surface,Predictive models | Journal | 17 |
Issue | ISSN | Citations |
8 | 1545-598X | 2 |
PageRank | References | Authors |
0.37 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kun Zhang | 1 | 2 | 0.37 |
| Xupu Geng | 2 | 15 | 2.03 |
| Xiao-Hai Yan | 3 | 20 | 7.36 |