Hybrid improved empirical mode decomposition and BP neural network model for the prediction of sea surface temperature

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成果类型：

期刊论文

作者：

Wu, Zhiyuan;Jiang, Changbo*;Conde, Mack;Deng, Bin;Chen, Jie

通讯作者：

Jiang, Changbo

作者机构：

[Jiang, Changbo; Wu, Zhiyuan; Deng, Bin; Chen, Jie] Changsha Univ Sci & Technol, Sch Hydraul Engn, Changsha, Hunan, Peoples R China.

[Wu, Zhiyuan] Univ Massachusetts Dartmouth, Sch Marine Sci & Technol, New Bedford, MA USA.

[Jiang, Changbo; Wu, Zhiyuan; Deng, Bin; Chen, Jie] Key Lab Water Sediment Sci & Water Disaster Preve, Changsha, Hunan, Peoples R China.

[Conde, Mack] Univ Massachusetts Dartmouth, Dept Math, N Dartmouth, MA USA.

通讯机构：

[Jiang, Changbo] C

[Jiang, Changbo] K

Changsha Univ Sci & Technol, Sch Hydraul Engn, Changsha, Hunan, Peoples R China.

Key Lab Water Sediment Sci & Water Disaster Preve, Changsha, Hunan, Peoples R China.

语种：

英文

期刊：

OCEAN SCIENCE

ISSN：

1812-0784

年：

2019

卷：

期：

页码：

349-360

DOI：

10.5194/os-15-349-2019

基金类别：

National Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [51809023, 51879015, 51839002, 51809021, 51509023]; Hunan Provincial Natural Science Foundation of ChinaNatural Science Foundation of Hunan Province [2018JJ3546]

机构署名：

本校为第一且通讯机构

院系归属：

水利工程学院

摘要：

Sea surface temperature (SST) is the major factor that affects the ocean-atmosphere interaction, and in turn the accurate prediction of SST is the key to ocean dynamic prediction. In this paper, an SST-predicting method based on empirical mode decomposition (EMD) algorithms and back-propagation neural network (BPNN) is proposed. Two different EMD algorithms have been applied extensively for analyzing time-series SST data and some nonlinear stochastic signals. The ensemble empirical mode decomposition (EEMD) algorithm and complementary ensemble ...

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Hybrid improved empirical mode decomposition and BP neural network model for the prediction of sea surface temperature

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