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

  • Jun Liu
  • , Tong Zhang
  • , Guangjie Han
  • , Yu Gou*
  • *Corresponding author for this work
  • College of Computer Science and Technology
  • CAS - Shenyang Institute of Automation
  • Dalian University of Technology

Research output: Contribution to journalArticlepeer-review

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 languageEnglish
Article number3797
JournalSensors
Volume18
Issue number11
DOIs
StatePublished - 5 Nov 2018
Externally publishedYes

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 13 - Climate Action
    SDG 13 Climate Action
  2. SDG 14 - Life Below Water
    SDG 14 Life Below Water

Keywords

  • Long short-term memory (LSTM)
  • Prediction
  • Sea surface temperature (SST)
  • Temporal dependence

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