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

被引:110
作者
Liu, Jun [1 ,2 ]
Zhang, Tong [1 ]
Han, Guangjie [3 ]
Gou, Yu [1 ]
机构
[1] Jilin Univ, Coll Comp Sci & Technol, Changchun 130012, Jilin, Peoples R China
[2] Chinese Acad Sci, Shenyang Inst Automat, State Key Lab Robot, Shenyang 110016, Liaoning, Peoples R China
[3] Dalian Univ Technol, Sch Software, Key Lab Ubiquitous Network & Serv Software Liaoni, Dalian 116024, Peoples R China
基金
中国国家自然科学基金;
关键词
long short-term memory (LSTM) temporal dependence; sea surface temperature (SST); prediction; FORECASTS; SST;
D O I
10.3390/s18113797
中图分类号
O65 [分析化学];
学科分类号
070302 [分析化学];
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.
引用
收藏
页数:13
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