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ORIGINAL RESEARCH article

Front. Mar. Sci.
Sec. Physical Oceanography
Volume 11 - 2024 | doi: 10.3389/fmars.2024.1424714

Predicting significant wave height in the South China Sea using the SAC-ConvLSTM model

Provisionally accepted
Boyang Hou Boyang Hou 1Hanjiao Fu Hanjiao Fu 1*Xin Li Xin Li 1Tao Song Tao Song 1*Zhiyuan Zhang Zhiyuan Zhang 2*
  • 1 China University of Petroleum(East China), Qingdao, Shandong Province, China
  • 2 The 91001 Unit of PLA, Beijing, China

The final, formatted version of the article will be published soon.

    The precise forecasting of Significant wave height(SWH) is vital to ensure the safety and efficiency of aquatic activities such as ocean engineering, shipping, and fishing. This paper proposes a deep learning model named SAC-ConvLSTM to perform 24-hour prediction with the significant wave height in the South China Sea. The long-term prediction capability of the model is enhanced by using the attention mechanism and context vectors. The experimental results show that the SAC-ConvLSTM model has the best prediction performance compared with other models, with RMSE, MAE, and PCC of 0.2117m, 0.1083m, and 0.9630, respectively, at the 24-hour prediction. In addition, the introduction of wind can improve the accuracy of wave prediction.The SAC-ConvLSTM model also has good prediction performance compared to the ConvLSTM model during extreme weather, especially in coastal areas.

    Keywords: Significant wave height forcast, deep learning, South China Sea, convolutional LSTM, attention mechanism

    Received: 28 Apr 2024; Accepted: 28 Jun 2024.

    Copyright: © 2024 Hou, Fu, Li, Song and Zhang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Hanjiao Fu, China University of Petroleum(East China), Qingdao, Shandong Province, China
    Tao Song, China University of Petroleum(East China), Qingdao, Shandong Province, China
    Zhiyuan Zhang, The 91001 Unit of PLA, Beijing, China

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