摘要
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.
| 源语言 | 英语 |
|---|---|
| 文章编号 | 3797 |
| 期刊 | Sensors |
| 卷 | 18 |
| 期 | 11 |
| DOI | |
| 出版状态 | 已出版 - 5 11月 2018 |
| 已对外发布 | 是 |
联合国可持续发展目标
此成果有助于实现下列可持续发展目标:
-
可持续发展目标 13 气候行动
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可持续发展目标 14 水下生物
指纹
探究 'TD-LSTM: Temporal dependence-based LSTM networks for marine temperature prediction' 的科研主题。它们共同构成独一无二的指纹。引用此
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