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EEMD-ConvLSTM: a model for short-term prediction of two-dimensional wind speed in the South China Sea

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Abstract

Accurate prediction of sea surface wind speed is crucial for marine activities such as marine search and rescue, marine shipping, and marine fishing. Because of the gustiness of sea surface winds, the wind speed data have strong non-stationarity and non-linearity, and it is still challenging to predict sea surface winds accurately and stably in a short time. Most previous studies have only considered the problem of non-smoothness of wind speed in single-point wind speed prediction, and this study extends the method to solve the non-smoothness of data in two dimensions. We proposed a hybrid model based on ensemble empirical mode decomposition (EEMD) and Convolutional long short-term memory network (ConvLSTM). Specifically, this study employs the Pearson correlation coefficient (PCC) to select the most periodic and most predictable subsequences of the EEMD signal decomposition, then fuse them into the spatio-temporal prediction of ConvLSTM. The method eliminates the influence of noise in the two-dimensional wind speed prediction and grasps the wind speed variation pattern more accurately. The proposed model has the best prediction effect based on the experimental findings, and this advantage becomes increasingly evident as time increases. This shows that EEMD signal decomposition is a good way to solve the short-term prediction problem for two-dimensional wind speed.

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Data Availability

The data used in this paper are publicly available and can be downloaded at https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=formdownload

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Acknowledgements

We thank Zhiyuan Zhang and Xin Li for their valuable comments on the article, which helped us to further understand the limitations of this paper and the direction of future work.

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Authors and Affiliations

  1. College of Computer Science and Technology, China University of Petroleum, 266580, Qingdao, China

    Handan Sun, Tao Song, Ying Li, Kunlin Yang & Fan Meng

  2. Department of Artificial Intelligence, Faculty of Computer Science, Polytechnical University of Madrid, 28660, Madrid, Spain

    Tao Song

  3. Guangdong Laboratory of Marine Science and Engineering, 519080, Zhuhai, China

    Danya Xu

  4. School of Artificial Intelligence, Nanjing University of Information Science and Technology, 210044, Nanjing, China

    Fan Meng

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  1. Handan Sun
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Correspondence to Tao Song or Fan Meng.

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Sun, H., Song, T., Li, Y. et al. EEMD-ConvLSTM: a model for short-term prediction of two-dimensional wind speed in the South China Sea. Appl Intell 53, 30186–30202 (2023). https://doi.org/10.1007/s10489-023-05042-0

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