Significant wave height prediction based on deep learning in the South China Sea
Significant wave height prediction based on deep learning in the South China Sea
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DOI：
10.3389/fmars.2022.1113788
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发表时间：
2023-02
期刊：
影响因子：
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通讯作者：
Peng Hao;Shuang Li;Yu Gao
中科院分区：
文献类型：
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作者：
Peng Hao;Shuang Li;Yu Gao
有效波高预测可以有效提高海上活动的安全性，减少海上事故的发生，对国家安全和海洋经济的发展具有重要意义。在这项研究中，我们通过考虑不同的输入长度，预测长度和模型复杂度，全面分析了递归神经网络（RNN），长短期记忆网络（LSTM）和门控递归单元网络（GRU）的SWH预测性能。实验结果表明：（1）输入长度对SWH的预测效果有影响，但并不意味着输入长度越长预测效果越好。当输入长度为24 h时，RNN、LSTM和GRU模型的预测性能更好。(2)预测长度影响SWH预测结果。随着预测长度的增加，预测性能逐渐降低。其中，RNN不适用于48 h长时SWH预测。(3)模型层数越多，SWH预测性能不一定越好。当层数设置为3或4时，模型的预测性能更好。
Significant wave height (SWH) prediction can effectively improve the safety of marine activities and reduce the occurrence of maritime accidents, which is of great significance to national security and the development of the marine economy. In this study, we comprehensively analyzed the SWH prediction performance of the recurrent neural network (RNN), long short-term memory network (LSTM), and gated recurrent unit network (GRU) by considering different input lengths, prediction lengths, and model complexity. The experimental results show that (1) the input length impacts the prediction results of SWH, but it does not mean that the longer the input length, the better the prediction performance. When the input length is 24h, the prediction performance of RNN, LSTM, and GRU models is better. (2) The prediction length influences the SWH prediction results. As the prediction length increases, the prediction performance gradually decreases. Among them, RNN is not suitable for 48h long-term SWH prediction. (3) The more layers of the model, the better the SWH prediction performance is not necessarily. When the number of layers is set to 3 or 4, the model’s prediction performance is better.