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.
| Original language | English |
|---|---|
| Article number | 3797 |
| Journal | Sensors |
| Volume | 18 |
| Issue number | 11 |
| DOIs | |
| State | Published - 5 Nov 2018 |
| Externally published | Yes |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 13 Climate Action
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SDG 14 Life Below Water
Keywords
- Long short-term memory (LSTM)
- Prediction
- Sea surface temperature (SST)
- Temporal dependence
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