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Separation method of wind-wave and swell based on the multilayer perceptron
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Xiao Xu1, 2, 4, Aifeng Tao1, 2, *, Xue Han3, Xishan Pan3, Yini Yang1, 2
Haiyang Xuebao | 2023, 45(2) : 1 - 12
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Haiyang Xuebao | 2023, 45(2): 1-12
• Article •
Separation method of wind-wave and swell based on the multilayer perceptron
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Xiao Xu1, 2, 4, Aifeng Tao1, 2, *, Xue Han3, Xishan Pan3, Yini Yang1, 2
Affiliations
  • 1Key Laboratory of Ministry of Education for Coastal Disaster and Protection, Hohai University, Nanjing 210024, China
  • 2College of Harbor, Coastal and Offshore Engineering, Hohai University, Nanjing 210024, China
  • 3Tidal Flat Research Center of Jiangsu Province, Nanjing 210036, China
  • 4Port and Waterway Development Center, Department of Transportation of Jiangsu Province, Nanjing 210004, China
Published: 2023-02-01 doi: 10.12284/hyxb2023001
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Separation of wind-wave and swell is the basis for studying the respective characteristics of wind-wave and swell. However, due to the lack of wave spectrum data, it is difficult to popularize and apply separation methods based on wave spectrums. An effective solution is to use wave observations that are easy to obtain, namely basic wave elements to separate wind-wave and swell. Existing methods cannot use basic wave elements to comprehensively calculate the proportions and characteristic parameters of wind-wave and swell. For this reason, this paper introduces machine learning into the separation of wind-wave and swell. Based on the multi-layer perceptron model, a method using wave elements and wind elements to accurately estimate wind-wave and swell parameters is proposed. This method requires each station to provide at least 466 training samples of wave data and 766 or more training samples are recommended. The method is suitable for 3 stations in the Taiwan Strait with its accuracy significantly better than traditional methods based on wave spectrums. The proposed method can provide alternative calculation schemes of wind-wave and swell for stations lacking wave spectrums in this sea area. It helps expand the source of measured data of wind-wave and swell, therefore strengthening the research on the characteristics and early warning and forecasting of wind-wave and swell.

separation of wind-wave and swell  /  Taiwan Strait  /  machine learning  /  swell  /  wind-wave
Xiao Xu, Aifeng Tao, Xue Han, Xishan Pan, Yini Yang. Separation method of wind-wave and swell based on the multilayer perceptron[J]. Haiyang Xuebao, 2023 , 45 (2) : 1 -12 . DOI: 10.12284/hyxb2023001
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Article Info
doi: 10.12284/hyxb2023001
  • Receive Date：2021-06-22
  • Online Date：2025-12-26
  • Published：2023-02-01
Article Data
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History
  • Received：2021-06-22
  • Revised：2022-07-23
Funding
Affiliations
    1Key Laboratory of Ministry of Education for Coastal Disaster and Protection, Hohai University, Nanjing 210024, China
    2College of Harbor, Coastal and Offshore Engineering, Hohai University, Nanjing 210024, China
    3Tidal Flat Research Center of Jiangsu Province, Nanjing 210036, China
    4Port and Waterway Development Center, Department of Transportation of Jiangsu Province, Nanjing 210004, China
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表12种不同金属材料的力学参数
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Family		 属数
Number of
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Number 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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