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TD-LSTM: Temporal dependence-based LSTM networks for marine temperature prediction

  • Jun Liu
  • , Tong Zhang
  • , Guangjie Han
  • , Yu Gou*
  • *此作品的通讯作者
  • College of Computer Science and Technology
  • CAS - Shenyang Institute of Automation
  • Dalian University of Technology

科研成果: 期刊稿件文章同行评审

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
18
11
DOI
出版状态已出版 - 5 11月 2018
已对外发布

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:

  1. 可持续发展目标 13 - 气候行动
    可持续发展目标 13 气候行动
  2. 可持续发展目标 14 - 水下生物
    可持续发展目标 14 水下生物

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