Articles | Volume 15, issue 2
https://doi.org/10.5194/os-15-349-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/os-15-349-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
05 Apr 2019
Research article |
| 05 Apr 2019
| 05 Apr 2019
Hybrid improved empirical mode decomposition and BP neural network model for the prediction of sea surface temperature
Zhiyuan Wu
School of Hydraulic Engineering, Changsha University of Science & Technology, Changsha, China
School for Marine Science and Technology, University of Massachusetts Dartmouth, New Bedford, MA, USA
Key Laboratory of Water-Sediment Sciences and Water Disaster Prevention of Hunan Province, Changsha, China
Changbo Jiang
CORRESPONDING AUTHOR
School of Hydraulic Engineering, Changsha University of Science & Technology, Changsha, China
Key Laboratory of Water-Sediment Sciences and Water Disaster Prevention of Hunan Province, Changsha, China
Mack Conde
Department of Mathematics, University of Massachusetts Dartmouth, North Dartmouth, MA, USA
Bin Deng
School of Hydraulic Engineering, Changsha University of Science & Technology, Changsha, China
Key Laboratory of Water-Sediment Sciences and Water Disaster Prevention of Hunan Province, Changsha, China
Jie Chen
School of Hydraulic Engineering, Changsha University of Science & Technology, Changsha, China
Key Laboratory of Water-Sediment Sciences and Water Disaster Prevention of Hunan Province, Changsha, China
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- Multifactor prediction of sea water quality based on improved K-LSTM Z. Xie et al. 10.1080/00150193.2022.2087246
- Systematic Literature Review of Various Neural Network Techniques for Sea Surface Temperature Prediction Using Remote Sensing Data L. Chaudhary et al. 10.1007/s11831-023-09970-5
- CoCluster-DAGCN: a dynamic aggregate graph convolution network by a co-attention LSTM cluster for ocean temperature predictions Y. Chen et al. 10.1007/s11042-023-15768-1
- Sensitivity of WRF simulated typhoon track and intensity over the South China Sea to horizontal and vertical resolutions Z. Wu et al. 10.1007/s13131-019-1459-z
- Application of BP Neural Networks in Tide Forecasting H. Xu et al. 10.3390/atmos13121999
- A hybrid decomposition-based Machine Learning approach for predicting subsurface temperature in the Arabian Sea A. Malavika et al. 10.1007/s40808-024-02167-0
- Forecasting sea surface temperature during typhoon events in the Bohai Sea using spatiotemporal neural networks H. He et al. 10.1016/j.atmosres.2024.107578
- Ocean Reanalysis Data‐Driven Deep Learning Forecast for Sea Surface Multivariate in the South China Sea Q. Shao et al. 10.1029/2020EA001558
- Development of heuristic neural network algorithm for the prognosis of underwater ocean parameters D. Menaka et al. 10.1007/s11001-022-09501-0
- Numerical investigation of Typhoon Kai-tak (1213) using a mesoscale coupled WRF-ROMS model — Part Ⅱ: Wave effects Z. Wu et al. 10.1016/j.oceaneng.2019.106805
- MIMO: A Unified Spatio-Temporal Model for Multi-Scale Sea Surface Temperature Prediction S. Hou et al. 10.3390/rs14102371
- Multivariate Sea Surface Prediction in the Bohai Sea Using a Data-Driven Model S. Hu et al. 10.3390/jmse11112096
- The 10.7‐cm radio flux multistep forecasting based on empirical mode decomposition and back propagation neural network J. Luo et al. 10.1002/tee.23092
- Prediction Method for Ocean Wave Height Based on Stacking Ensemble Learning Model Y. Zhan et al. 10.3390/jmse10081150
- Global monthly sea surface temperature forecasting using the SARIMA, LSTM, and GRU models M. Bilgili et al. 10.1007/s12145-024-01585-z
- Dynamic graphs attention for ocean variable forecasting J. Wang et al. 10.1016/j.engappai.2024.108187
- Nonlinear system modeling and application based on restricted Boltzmann machine and improved BP neural network J. Qiao & L. Wang 10.1007/s10489-019-01614-1
- A review of artificial intelligence in marine science T. Song et al. 10.3389/feart.2023.1090185
- Green Supply Chain Optimization Based on BP Neural Network H. Wang 10.3389/fnbot.2022.865693
- The long-term spatiotemporal variability of sea surface temperature in the northwest Pacific and China offshore Z. Wu et al. 10.5194/os-16-83-2020
- Empirical mode modeling J. Park et al. 10.1007/s11071-022-07311-y
- A Hybrid ARIMA-GABP Model for Predicting Sea Surface Temperature X. Chen et al. 10.3390/electronics11152359
- Prediction of seawater pH by bidirectional gated recurrent neural network with attention under phase space reconstruction: case study of the coastal waters of Beihai, China C. Xu et al. 10.1007/s13131-023-2149-y
- Application of Fast MEEMD–ConvLSTM in Sea Surface Temperature Predictions R. Wanigasekara et al. 10.3390/rs16132468
- Simulation of Marine Weather during an Extreme Rainfall Event: A Case Study of a Tropical Cyclone Z. Wu & N. Alshdaifat 10.3390/hydrology6020042
- Multistep-Ahead Prediction of Ocean SSTA Based on Hybrid Empirical Mode Decomposition and Gated Recurrent Unit Model X. Liu et al. 10.1109/JSTARS.2022.3201228
- Prediction of Dominant Ocean Parameters for Sustainable Marine Environment D. Menaka & S. Gauni 10.1109/ACCESS.2021.3122237
- Contribution of Atmospheric Factors in Predicting Sea Surface Temperature in the East China Sea Using the Random Forest and SA-ConvLSTM Model Q. Ji et al. 10.3390/atmos15060670
30 citations as recorded by crossref.
- Forecasting of Mesoscale Eddies in the Kuroshio Extension Based on Temporal Modes-Enhanced Neural Network H. Wang et al. 10.3390/jmse11112201
- ILF-LSTM: enhanced loss function in LSTM to predict the sea surface temperature B. Usharani 10.1007/s00500-022-06899-y
- Multifactor prediction of sea water quality based on improved K-LSTM Z. Xie et al. 10.1080/00150193.2022.2087246
- Systematic Literature Review of Various Neural Network Techniques for Sea Surface Temperature Prediction Using Remote Sensing Data L. Chaudhary et al. 10.1007/s11831-023-09970-5
- CoCluster-DAGCN: a dynamic aggregate graph convolution network by a co-attention LSTM cluster for ocean temperature predictions Y. Chen et al. 10.1007/s11042-023-15768-1
- Sensitivity of WRF simulated typhoon track and intensity over the South China Sea to horizontal and vertical resolutions Z. Wu et al. 10.1007/s13131-019-1459-z
- Application of BP Neural Networks in Tide Forecasting H. Xu et al. 10.3390/atmos13121999
- A hybrid decomposition-based Machine Learning approach for predicting subsurface temperature in the Arabian Sea A. Malavika et al. 10.1007/s40808-024-02167-0
- Forecasting sea surface temperature during typhoon events in the Bohai Sea using spatiotemporal neural networks H. He et al. 10.1016/j.atmosres.2024.107578
- Ocean Reanalysis Data‐Driven Deep Learning Forecast for Sea Surface Multivariate in the South China Sea Q. Shao et al. 10.1029/2020EA001558
- Development of heuristic neural network algorithm for the prognosis of underwater ocean parameters D. Menaka et al. 10.1007/s11001-022-09501-0
- Numerical investigation of Typhoon Kai-tak (1213) using a mesoscale coupled WRF-ROMS model — Part Ⅱ: Wave effects Z. Wu et al. 10.1016/j.oceaneng.2019.106805
- MIMO: A Unified Spatio-Temporal Model for Multi-Scale Sea Surface Temperature Prediction S. Hou et al. 10.3390/rs14102371
- Multivariate Sea Surface Prediction in the Bohai Sea Using a Data-Driven Model S. Hu et al. 10.3390/jmse11112096
- The 10.7‐cm radio flux multistep forecasting based on empirical mode decomposition and back propagation neural network J. Luo et al. 10.1002/tee.23092
- Prediction Method for Ocean Wave Height Based on Stacking Ensemble Learning Model Y. Zhan et al. 10.3390/jmse10081150
- Global monthly sea surface temperature forecasting using the SARIMA, LSTM, and GRU models M. Bilgili et al. 10.1007/s12145-024-01585-z
- Dynamic graphs attention for ocean variable forecasting J. Wang et al. 10.1016/j.engappai.2024.108187
- Nonlinear system modeling and application based on restricted Boltzmann machine and improved BP neural network J. Qiao & L. Wang 10.1007/s10489-019-01614-1
- A review of artificial intelligence in marine science T. Song et al. 10.3389/feart.2023.1090185
- Green Supply Chain Optimization Based on BP Neural Network H. Wang 10.3389/fnbot.2022.865693
- The long-term spatiotemporal variability of sea surface temperature in the northwest Pacific and China offshore Z. Wu et al. 10.5194/os-16-83-2020
- Empirical mode modeling J. Park et al. 10.1007/s11071-022-07311-y
- A Hybrid ARIMA-GABP Model for Predicting Sea Surface Temperature X. Chen et al. 10.3390/electronics11152359
- Prediction of seawater pH by bidirectional gated recurrent neural network with attention under phase space reconstruction: case study of the coastal waters of Beihai, China C. Xu et al. 10.1007/s13131-023-2149-y
- Application of Fast MEEMD–ConvLSTM in Sea Surface Temperature Predictions R. Wanigasekara et al. 10.3390/rs16132468
- Simulation of Marine Weather during an Extreme Rainfall Event: A Case Study of a Tropical Cyclone Z. Wu & N. Alshdaifat 10.3390/hydrology6020042
- Multistep-Ahead Prediction of Ocean SSTA Based on Hybrid Empirical Mode Decomposition and Gated Recurrent Unit Model X. Liu et al. 10.1109/JSTARS.2022.3201228
- Prediction of Dominant Ocean Parameters for Sustainable Marine Environment D. Menaka & S. Gauni 10.1109/ACCESS.2021.3122237
- Contribution of Atmospheric Factors in Predicting Sea Surface Temperature in the East China Sea Using the Random Forest and SA-ConvLSTM Model Q. Ji et al. 10.3390/atmos15060670
Latest update: 20 Jan 2025
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...
