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Ocean Science An interactive open-access journal of the European Geosciences Union
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OS | Articles | Volume 15, issue 2
Ocean Sci., 15, 349–360, 2019
https://doi.org/10.5194/os-15-349-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Ocean Sci., 15, 349–360, 2019
https://doi.org/10.5194/os-15-349-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 05 Apr 2019

Research article | 05 Apr 2019

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

Zhiyuan Wu et al.

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Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Zhiyuan Wu on behalf of the Authors (12 Feb 2019)  Author's response    Manuscript
ED: Publish subject to minor revisions (review by editor) (20 Feb 2019) by John M. Huthnance
AR by Zhiyuan Wu on behalf of the Authors (02 Mar 2019)  Author's response    Manuscript
ED: Publish subject to minor revisions (review by editor) (08 Mar 2019) by John M. Huthnance
AR by Zhiyuan Wu on behalf of the Authors (12 Mar 2019)  Author's response    Manuscript
ED: Publish subject to technical corrections (19 Mar 2019) by John M. Huthnance
Publications Copernicus
Short summary
Sea surface temperature (SST) is related to ocean heat content, an important topic in the debate over global warming. In this paper, we propose a novel SST-predicting method based on the hybrid improved EMD algorithms and BP neural network method. SST prediction results based on the hybrid EEMD-BPNN and CEEMD-BPNN models are compared and discussed. A case study of SST in the North Pacific shows that the proposed hybrid CEEMD-BPNN model can effectively predict the time-series SST.
Sea surface temperature (SST) is related to ocean heat content, an important topic in the debate...
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