Research on multivariate Yellow Sea SST week prediction method based on encoder-decoder LSTM

SUN Jiyu1,2ZHU Zemin1,2SONG Jun1,2GUO Junru1,2CAI Yu1,2FU Yanzhao1,2Wang Linhui1,2, Polonsky A.3

 1Dalian Ocean University, School of Ocean Technology and Environment,  Dalian, Liaoning 116023

2Operational Oceanographic Institution, Dalian Ocean University,  Dalian, Liaoning 116023

3Institute of Natural and Technical Systems, RF, Sevastopol, 28 Lenin St. 299011


DOI: 10.33075/2220-5861-2022-1-5-14


   In order to further improve the accuracy and stability of Yellow Sea surface temperature forecasting, this paper uses the 25-year data of OISST V2.0 and OAFlux. The following factors (determining a sea surface temperature – SST – variability) are considered: radiation and total heat fluxes, wind speed, air temperature and air specific humidity. By controlling the variables and selecting the best model parameters, a multivariate weekly SST prediction model based on the Encoder-Decoder LSTM (Long Short-Term Memory) was constructed for the first time. It is shown that the model can effectively track the daily SST changes and describe its fluctuation changes to achieve relatively accurate prediction.  Taking 2008 as an example the following result is obtained: the daily absolute errors of the test set within a week are 0.3836, 0.4523, 0.5276, 0.5905, 0.6362, 0.6644, and 0.6827, and the overall standard error (RMSE) is 0.7594. It is concluded, that further research is needed on the optimization of predictors and the applicability of single-point forecasting of the model in the SST prediction.

Keywords: LSTM; SST; Yellow Sea; artificial intelligence method; SST forecast.

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  1. Patil K., Deo M. C., Ravichandran M. Prediction of Sea Surface Temperature by Combining Numerical and Neural Techniques // Journal of Atmospheric and Oceanic Technology. 2016. Vol. 33 (8). P. 1715–1726.
  2. 张建华. 海温预报知识讲座:第一讲 海水温度预报概况 // 海洋预报. 2003. Vol. 20 (4). P. 81–85. (Zhang Jianhua., SST forecast knowledge lecture: the first lecture sea water temperature forecast overview // MARINE FORECASTS. 2003. Vol. 20 (4). P. 81–85).
  3. Zhang Q., Wang H., Dong J., et al. Prediction of Sea Surface Temperature Using Long Short-Term Memory // IEEE Geoscience and Remote Sensing Letters. 2017. Vol. 14 (10). P. 1745–1749.
  4. Xike Z., Qiuwen Z., Gui Z., et al. A Novel Hybrid Data-Driven Model for Daily Land Surface Temperature Forecasting Using Long Short-Term Memory Neural Network Based on Ensemble Empirical Mode Decomposition // International Journal of Environmental Research and Public Health. 2018. Vol 15 (5). P. 1032.
  5. Song G., Peng Z., Bin P., et al. A nowcasting model for the prediction of typhoon tracks based on a long short term memory neural network // 海洋学报(英文版). 2018. Vol. 37 (5).
  6. Zhanga J., Zhub Y., Zhanga X., et al. Developing a Long Short-Term Memory (LSTM) based model for predicting water table depth in agricultural areas // Journal of Hydrology. 2018. P. 561.
  7. Reddy D. S., Prasad P. R.C. Prediction of vegetation dynamics using NDVI time series data and LSTM // Modeling Earth Systems and Environment. 2018.
  8. Asanjan A. A., Yang T., Hsu K., et al. Short-term Precipitation Forecast based on the PERSIANN system and the Long Short-Term Memory (LSTM) Deep Learning Algorithm // Journal of Geophysical Research Atmospheres. 2018.
  9. 白盛楠, 申晓留. 基于LSTM循环神经网络的PM_(2.5)预测 // 计算机应用与软件. 2019. Vol. 36 (01). P. 73–76. (Bai Shengnan., Shen Xiaoliu., PM2.5 Prediction based on LSTM Recurrent Neural Net-Work // Computer Applications and Software. 2019. Vol. 36 (01). P. 73–76).
  10. Polonsky A. The Ocean’s Role in Climate Change. Cambridge Scholars Publishing. Newcastle. UK. 2021. 290 p.
  11. Polonsky A.B., Serebrennikov A.N. Interannual and Intra-Monthly Fluctuations of the Wind Field and Sea Surface Temperature in the West African Region Based on Satellite Data // Izv., Atm. and Ocean Physics, Vol. 54, No. 9, 2018, pp. 1057–1061.
  12. Hochreiter S., Schmidhuber J. Long Short-Term Memory // Neural Computation. 1997. Vol. 9 (8). P. 1735–1780.
  13. 周林, 杨成荫, 王汉杰, et al. 基于CCA-BP-BPNN释用模型的太平洋SST预报 // 解放军理工大学学报(自然科学版). 2009. Vol. 10 (4). P. 391–396. (Zhou Lin., Yang Chengyin., Wang Hanjie., et al. lnterpretation scheme of SST prediction in the tropical Pacific Ocean based on CCA-BP-BPNN // Journal of PLA University of Science and Technology (Natural Science Edition). 2009. Vol. 10 (4). P. 391–396).