A Prediction Model of Significant Wave Height in the South China Sea Based on Attention Mechanism

被引:12
|
作者
Hao, Peng [1 ]
Li, Shuang [1 ]
Yu, Chengcheng [1 ]
Wu, Gengkun [2 ]
机构
[1] Zhejiang Univ, Inst Phys Oceanog & Remote Sensing, Ocean Coll, Zhoushan, Peoples R China
[2] Shandong Univ Sci & Technol, Coll Comp Sci & Engn, Qingdao, Peoples R China
基金
中国国家自然科学基金;
关键词
CBA-Net; significant wave height (SWH); deep learning; South China Sea; attention mechanism; STATISTICAL-MODELS; TERM PREDICTION; NEURAL-NETWORKS; SIMULATION; FORECASTS;
D O I
10.3389/fmars.2022.895212
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Significant wave height (SWH) prediction plays an important role in marine engineering fields such as fishery, exploration, power generation, and ocean transportation. Traditional SWH prediction methods based on numerical models cannot achieve high accuracy. In addition, the current SWH prediction methods are largely limited to single-point SWH prediction, without considering regional SWH prediction. In order to explore a new SWH prediction method, this paper proposes a deep neural network model for regional SWH prediction based on the attention mechanism, namely CBA-Net. In this study, the wind and wave height of the ERA5 data set in the South China Sea from 2011 to 2018 were used as input features to train the model to evaluate the SWH prediction performance at 1 h, 12 h, and 24 h. The results show that the single use of a convolutional neural network cannot accurately predict SWH. After adding the Bi-LSTM layer and attention mechanism, the prediction of SWH is greatly improved. In the 1 h SWH prediction using CBA-Net, SARMSE, SAMAPE, SACC are 0.299, 0.136, 0.971 respectively. Compared with the CNN + Bi-LSTM method that does not use the attention mechanism, SARMSE and SAMAPE are reduced by 43.4% and 48.7%, respectively, while SACC is increased by 5%. In the 12 h SWH prediction, SARMSE, SAMAPE, and SACC of CBA-Net are 0.379, 0.177, 0.954 respectively. In the 24 h SWH prediction, SARMSE, SAMAPE, and SACC of CBA-Net are 0.500, 0.236, 0.912 respectively. Although with the increase of prediction time, the performance is slightly lower than that of 12 h, the prediction error is still maintained at a small level, which is still better than other methods.
引用
收藏
页数:12
相关论文
共 50 条
  • [1] Significant wave height prediction based on deep learning in the South China Sea
    Hao, Peng
    Li, Shuang
    Gao, Yu
    FRONTIERS IN MARINE SCIENCE, 2023, 9
  • [2] Significant Wave Height Prediction in the South China Sea Based on the ConvLSTM Algorithm
    Han, Lei
    Ji, Qiyan
    Jia, Xiaoyan
    Liu, Yu
    Han, Guoqing
    Lin, Xiayan
    JOURNAL OF MARINE SCIENCE AND ENGINEERING, 2022, 10 (11)
  • [3] Predicting significant wave height in the South China Sea using the SAC-ConvLSTM model
    Hou, Boyang
    Fu, Hanjiao
    Li, Xin
    Song, Tao
    Zhang, Zhiyuan
    FRONTIERS IN MARINE SCIENCE, 2024, 11
  • [4] Prediction of Significant Wave Heights Based on CS-BP Model in the South China Sea
    Yang, Shaobo
    Xia, Tianliang
    Zhang, Zhenquan
    Zheng, Chongwei
    Li, Xingfei
    Li, Hongyu
    Xu, Jianjun
    IEEE ACCESS, 2019, 7 : 147490 - 147500
  • [5] Sea Surface Height Prediction With Deep Learning Based on Attention Mechanism
    Liu, Jingjing
    Jin, Baogang
    Wang, Lei
    Xu, Lingyu
    IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 2022, 19
  • [6] Significant wave height modelling and simulation of the monsoon-influenced South China Sea coast
    Nasir, Faerah
    Taib, Che Mohd Imran Che
    Ariffin, Effi Helmy
    Padlee, Siti Falindah
    Akhir, Mohd Fadzil
    Ahmad, Mohammad Fadhli
    Yusoff, Binyamin
    OCEAN ENGINEERING, 2023, 277
  • [7] Dynamics of the seasonal wave height variability in the South China Sea
    Zhai, Fangguo
    Wu, Wenfan
    Gu, Yanzhen
    Li, Peiliang
    Liu, Zizhou
    INTERNATIONAL JOURNAL OF CLIMATOLOGY, 2021, 41 (02) : 934 - 951
  • [8] Significant Wave Height Prediction Based on MSFD Neural Network
    Wang, Huan
    Fu, Dongyang
    Liao, Shan
    Wang, Guancheng
    Xiao, Xiuchun
    2019 TENTH INTERNATIONAL CONFERENCE ON INTELLIGENT CONTROL AND INFORMATION PROCESSING (ICICIP), 2019, : 39 - 43
  • [9] Prediction of Significant Wave Height in Offshore China Based on the Machine Learning Method
    Feng, Zhijie
    Hu, Po
    Li, Shuiqing
    Mo, Dongxue
    JOURNAL OF MARINE SCIENCE AND ENGINEERING, 2022, 10 (06)
  • [10] A Machine-Learning Approach Based on Attention Mechanism for Significant Wave Height Forecasting
    Shi, Jiao
    Su, Tianyun
    Li, Xinfang
    Wang, Fuwei
    Cui, Jingjing
    Liu, Zhendong
    Wang, Jie
    JOURNAL OF MARINE SCIENCE AND ENGINEERING, 2023, 11 (09)