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Sensors 2018, 18(11), 3797;

TD-LSTM: Temporal Dependence-Based LSTM Networks for Marine Temperature Prediction

College of Computer Science and Technology, Jilin University, Changchun 130012, China
State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province, School of Software, Dalian University of Technology, Dalian 116024, China
Author to whom correspondence should be addressed.
Received: 10 October 2018 / Revised: 27 October 2018 / Accepted: 2 November 2018 / Published: 6 November 2018
(This article belongs to the Special Issue Smart Ocean: Emerging Research Advances, Prospects and Challenges)
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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. View Full-Text
Keywords: long short-term memory (LSTM); temporal dependence; sea surface temperature (SST); prediction long short-term memory (LSTM); temporal dependence; sea surface temperature (SST); prediction

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Liu, J.; Zhang, T.; Han, G.; Gou, Y. TD-LSTM: Temporal Dependence-Based LSTM Networks for Marine Temperature Prediction. Sensors 2018, 18, 3797.

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