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

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 depth...

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Detalles Bibliográficos
Autores principales: Liu, Jun, Zhang, Tong, Han, Guangjie, Gou, Yu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6263690/
https://www.ncbi.nlm.nih.gov/pubmed/30404217
http://dx.doi.org/10.3390/s18113797
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author Liu, Jun
Zhang, Tong
Han, Guangjie
Gou, Yu
author_facet Liu, Jun
Zhang, Tong
Han, Guangjie
Gou, Yu
author_sort Liu, Jun
collection PubMed
description 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.
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spelling pubmed-62636902018-12-12 TD-LSTM: Temporal Dependence-Based LSTM Networks for Marine Temperature Prediction Liu, Jun Zhang, Tong Han, Guangjie Gou, Yu Sensors (Basel) Article 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. MDPI 2018-11-06 /pmc/articles/PMC6263690/ /pubmed/30404217 http://dx.doi.org/10.3390/s18113797 Text en © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Liu, Jun
Zhang, Tong
Han, Guangjie
Gou, Yu
TD-LSTM: Temporal Dependence-Based LSTM Networks for Marine Temperature Prediction
title TD-LSTM: Temporal Dependence-Based LSTM Networks for Marine Temperature Prediction
title_full TD-LSTM: Temporal Dependence-Based LSTM Networks for Marine Temperature Prediction
title_fullStr TD-LSTM: Temporal Dependence-Based LSTM Networks for Marine Temperature Prediction
title_full_unstemmed TD-LSTM: Temporal Dependence-Based LSTM Networks for Marine Temperature Prediction
title_short TD-LSTM: Temporal Dependence-Based LSTM Networks for Marine Temperature Prediction
title_sort td-lstm: temporal dependence-based lstm networks for marine temperature prediction
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6263690/
https://www.ncbi.nlm.nih.gov/pubmed/30404217
http://dx.doi.org/10.3390/s18113797
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