| TD-LSTM: Temporal dependence-based LSTM networks for marine temperature prediction | |
Liu J(刘军)1,2 ; Zhang, Tong1; Han, Guangjie3; Gou, Yu1
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| Department | 机器人学研究室 |
| Source Publication | Sensors (Switzerland)
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| ISSN | 1424-8220 |
| 2018 | |
| Volume | 18Issue:11Pages:1-13 |
| Indexed By | SCI ; EI |
| EI Accession number | 20184606072266 |
| WOS ID | WOS:000451598900207 |
| Contribution Rank | 1 |
| Funding Organization | National Natural Science Foundation of China ; Fundamental Research Funds for the Central Universities ; State Key Laboratory of Robotics ; National Natural Science Foundation of China-Guangdong Joint Fund ; program for Liaoning Excellent Talents in University |
| Keyword | Long Short-term Memory (Lstm) Temporal Dependence Sea Surface Temperature (Sst) Prediction |
| 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. |
| Language | 英语 |
| WOS Subject | Chemistry, Analytical ; Electrochemistry ; Instruments & Instrumentation |
| WOS Keyword | FORECASTS ; SST |
| WOS Research Area | Chemistry ; Electrochemistry ; Instruments & Instrumentation |
| Funding Project | National Natural Science Foundation of China[61631008] ; National Natural Science Foundation of China[61572172] ; National Natural Science Foundation of China[61872124] ; Fundamental Research Funds for the Central Universities[2017TD-18] ; Fundamental Research Funds for the Central Universities[DUT17RC(3)094] ; State Key Laboratory of Robotics[2015-O06] ; National Natural Science Foundation of China-Guangdong Joint Fund[U180120020] ; program for Liaoning Excellent Talents in University[LR2017009] |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.sia.cn/handle/173321/23582 |
| Collection | 机器人学研究室 |
| Corresponding Author | Gou, Yu |
| Affiliation | 1.College of Computer Science and Technology, Jilin University, Changchun 130012, China 2.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China 3.Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province, School of Software, Dalian University of Technology, Dalian 116024, China |
| Recommended Citation GB/T 7714 | Liu J,Zhang, Tong,Han, Guangjie,et al. TD-LSTM: Temporal dependence-based LSTM networks for marine temperature prediction[J]. Sensors (Switzerland),2018,18(11):1-13. |
| APA | Liu J,Zhang, Tong,Han, Guangjie,&Gou, Yu.(2018).TD-LSTM: Temporal dependence-based LSTM networks for marine temperature prediction.Sensors (Switzerland),18(11),1-13. |
| MLA | Liu J,et al."TD-LSTM: Temporal dependence-based LSTM networks for marine temperature prediction".Sensors (Switzerland) 18.11(2018):1-13. |
| Files in This Item: | ||||||
| File Name/Size | DocType | Version | Access | License | ||
| TD-LSTM_ Temporal de（2334KB） | 期刊论文 | 出版稿 | 开放获取 | CC BY-NC-SA | View Application Full Text | |
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