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Ocean Science An interactive open-access journal of the European Geosciences Union
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Discussion papers
https://doi.org/10.5194/os-2018-101
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/os-2018-101
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 28 Nov 2018

Research article | 28 Nov 2018

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Ocean Science (OS).

Hybrid improved EMD-BPNN model for the prediction of sea surface temperature

Zhiyuan Wu1,2,3, Changbo Jiang1,3, Mack Conde4, Bin Deng1,3, and Jie Chen1,3 Zhiyuan Wu et al.
  • 1School of Hydraulic Engineering, Changsha University of Science & Technology, Changsha, 410004, China
  • 2School for Marine Science and Technology, University of Massachusetts Dartmouth, New Bedford, MA 02744, USA
  • 3Key Laboratory of Water-Sediment Sciences and Water Disaster Prevention of Hunan Province, Changsha, 410004, China
  • 4Department of Mathematics, University of Massachusetts Dartmouth, North Dartmouth, MA 02747, USA

Abstract. 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 improved 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. Ensemble empirical mode decomposition (EEMD) algorithm and Complementary Ensemble Empirical Mode Decomposition (CEEMD) algorithm are two improved algorithms of EMD, which can effectively handle the mode-mixing problem and decompose the original data into more stationary signals with different frequencies. Each Intrinsic Mode Function (IMF) has been taken as an input data to the back-propagation neural network model. The final predicted SST data is obtained by aggregating the predicted data of individual IMF. A case study, of the monthly mean sea surface temperature anomaly (SSTA) in the northeastern region of the North Pacific, shows that the proposed hybrid CEEMD-BPNN model is much more accurate than the hybrid EEMD-BPNN model, and the prediction accuracy based on BP neural network is improved by the CEEMD method. Statistical analysis of the case study demonstrates that applying the proposed hybrid CEEMD-BPNN model is effective for the SST prediction.

Zhiyuan Wu et al.
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Short summary
Sea surface temperature (SST) is related to ocean heat content, an important topic in the debate over global warming. In this manuscript, 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. Cases 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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