Name
Abstract | ||
|---|---|---|
Changes in ocean temperature over time have important implications for marine ecosystems and global climate change. Marine temperature changes with time and has the features of closeness, period, and trend. This paper analyzes the temporal dependence of marine temperature variation at multiple depths and proposes a new ocean-temperature time-series prediction method based on the temporal dependence parameter matrix fusion of historical observation data. The Temporal Dependence-Based Long Short-Term Memory (LSTM) Networks for Marine Temperature Prediction (TD-LSTM) proves better than other methods while predicting sea-surface temperature (SST) by using Argo data. The performances were good at various depths and different regions. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.3390/s18113797 | SENSORS |
Keywords | Field | DocType |
long short-term memory (LSTM) temporal dependence,sea surface temperature (SST),prediction | Electronic engineering,Artificial intelligence,Engineering,Machine learning | Journal |
Volume | Issue | ISSN |
18 | 11.0 | 1424-8220 |
Citations | PageRank | References |
1 | 0.37 | 8 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jun Liu | 1 | 102 | 18.78 |
| Tong Zhang | 2 | 53 | 18.56 |
| Guangjie Han | 3 | 1890 | 172.76 |
| Yu Gou | 4 | 5 | 1.98 |