Short and mid-term sea surface temperature prediction using time-series satellite data and LSTM-AdaBoost combination approach-Reference-Cited by-同舟云学术

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Short and mid-term sea surface temperature prediction using time-series satellite data and LSTM-AdaBoost combination approach

Published:2019-11 Issue: Volume:233 Page:111358
ISSN:0034-4257
Container-title:Remote Sensing of Environment
language:en
Short-container-title:Remote Sensing of Environment

Author:

Xiao Changjiang,Chen Nengcheng^ORCID,Hu Chuli,Wang Ke,Gong Jianya,Chen Zeqiang^ORCID

Funder

National Natural Science Foundation of China

Fundamental Research Funds for the Central Universities

China Scholarship Council

Publisher

Elsevier BV

Subject

Computers in Earth Sciences,Geology,Soil Science

Reference59 articles.

1. TensorFlow: a system for large-scale machine learning;Abadi,2016

2. Analysis of SMOS sea surface salinity data using DINEOF;Alvera-Azcárate;Remote Sens. Environ.,2016

3. Prediction of daily sea surface temperature using artificial neural networks;Aparna;Int. J. Remote Sens.,2018

4. Temporal trends in sea surface temperature gradients in the South Atlantic Ocean;Bouali;Remote Sens. Environ.,2017

5. Interannual anomalies of SMOS sea surface salinity;Boutin;Remote Sens. Environ.,2016

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