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A multivariate wave forecasting model for the Zhoushan archipelago using Long Short-Term Memory deep neural networks
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Sangjun Zhou1, 2, Xiaoran Wei1, Xinzhe Xie1, 2, Honghuan Zhi1, Yifan Zhou1, Zhengtao Zhu3, Peiliang Li1, 2, Yefei Bai1, 2, *
Haiyang Xuebao | 2024, 46(6) : 14 - 25
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Haiyang Xuebao | 2024, 46(6): 14-25
• Article •
A multivariate wave forecasting model for the Zhoushan archipelago using Long Short-Term Memory deep neural networks
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Sangjun Zhou1, 2, Xiaoran Wei1, Xinzhe Xie1, 2, Honghuan Zhi1, Yifan Zhou1, Zhengtao Zhu3, Peiliang Li1, 2, Yefei Bai1, 2, *
Affiliations
  • 1. Ocean College, Zhejiang University, Zhoushan 316021, China
  • 2. Hainan Institute of Zhejiang University, Zhejiang University, Sanya 572025, China
  • 3. Institute of Space Technology, China Aerodynamics Research and Development Center, Mianyang 621000, China
Published: 2024-06-30 doi: 10.12284/hyxb2024049
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This study is based on the meteorological, oceanic, terrain and other physical quantity data covered by the observation points in the southern Zhoushan Islands from January 1, 2019 to December 31, 2021, and uses long short-term memory neural network (LSTM) to build deep learning wave forecast model. We explore the impact of the input-output sequence ratio and the number of input elements on the prediction performance of the model, realize the short-term forecast of the three elements of waves in the Zhoushan sea area, that is the significant wave height, the significant wave period and the propagation direction, and use the data during the 2022 typhoons “Hinnamnor” and “Muifa” to test the model’s prediction ability for extreme sea conditions. The research results show that the multi-element deep learning wave forecast model trained based on measured data has good prediction accuracy and stability, and can realize the prediction of extreme sea conditions. When the input-output sequence ratio is 1∶1, the model accuracy is higher. In non-extreme sea conditions, the three-element model with a prediction time of 1 hour accurately predicts significant wave height, significant wave period and direction, with Root Mean Squared Errors (RMSE) of 0.116 m, 0.569 s, and 24.583° respectively. In extreme sea conditions, the prediction RMSE for the significant wave height is 0.191 m. The increase in the number of input elements can further improve the model accuracy but also increase the training cost when the prediction time is long.

deep learning  /  Long Short-Term Memory model  /  wave forecasting  /  Zhoushan
Sangjun Zhou, Xiaoran Wei, Xinzhe Xie, Honghuan Zhi, Yifan Zhou, Zhengtao Zhu, Peiliang Li, Yefei Bai. A multivariate wave forecasting model for the Zhoushan archipelago using Long Short-Term Memory deep neural networks[J]. Haiyang Xuebao, 2024 , 46 (6) : 14 -25 . DOI: 10.12284/hyxb2024049
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Article Info
doi: 10.12284/hyxb2024049
  • Receive Date：2024-02-22
  • Online Date：2025-11-26
  • Published：2024-06-30
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History
  • Received：2024-02-22
  • Revised：2024-05-10
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Affiliations
    1. Ocean College, Zhejiang University, Zhoushan 316021, China
    2. Hainan Institute of Zhejiang University, Zhejiang University, Sanya 572025, China
    3. Institute of Space Technology, China Aerodynamics Research and Development Center, Mianyang 621000, China
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表12种不同金属材料的力学参数
科
Family		 属数
Number of
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Number of
species		 占总种数比例
Percentage of
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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