Journal cover Journal topic
Ocean Science An interactive open-access journal of the European Geosciences Union
Journal topic

Journal metrics

Journal metrics

  • IF value: 2.864 IF 2.864
  • IF 5-year value: 3.337 IF 5-year
    3.337
  • CiteScore value: 4.5 CiteScore
    4.5
  • SNIP value: 1.259 SNIP 1.259
  • IPP value: 3.07 IPP 3.07
  • SJR value: 1.326 SJR 1.326
  • Scimago H <br class='hide-on-tablet hide-on-mobile'>index value: 52 Scimago H
    index 52
  • h5-index value: 30 h5-index 30
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.

Viewed

Total article views: 1,513 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,065 419 29 1,513 36 37
  • HTML: 1,065
  • PDF: 419
  • XML: 29
  • Total: 1,513
  • BibTeX: 36
  • EndNote: 37
Views and downloads (calculated since 28 Nov 2018)
Cumulative views and downloads (calculated since 28 Nov 2018)

Viewed (geographical distribution)

Total article views: 1,010 (including HTML, PDF, and XML) Thereof 1,005 with geography defined and 5 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Saved (final revised paper)

No saved metrics found.

Saved (preprint)

No saved metrics found.

Discussed (final revised paper)

No discussed metrics found.

Discussed (preprint)

No discussed metrics found.
Latest update: 11 Aug 2020
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...
Citation