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Prediction of transmission coefficient of double-row perforated cylinder breakwater based on SSA-CNN model
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Bin Deng1, 2, 4, Ling Wang1, Jun He3, *, Longbin Yin1, Changbo Jiang1, 2, Jie Chen1, 2, Zhiyuan Wu1, 2
Haiyang Xuebao | 2024, 46(4) : 122 - 132
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Haiyang Xuebao | 2024, 46(4): 122-132
• Article •
Prediction of transmission coefficient of double-row perforated cylinder breakwater based on SSA-CNN model
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Bin Deng1, 2, 4, Ling Wang1, Jun He3, *, Longbin Yin1, Changbo Jiang1, 2, Jie Chen1, 2, Zhiyuan Wu1, 2
Affiliations
  • 1. School of Hydraulic and Environmental Engineering, Changsha University of Science & Technology, Changsha 410114, China
  • 2. Key Laboratory of Water-Sediment Sciences and Water Disaster Prevention of Hunan Province, Changsha 410114, China
  • 3. CCCC Water Transportation Consultants Co., Ltd., Beijing 100007, China
  • 4. State Key Laboratory of Hydraulic Engineering Intelligent Construction and Operation, Tianjin 300072, China
Published: 2024-04-30 doi: 10.12284/hyxb2024035
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The double-row perforated cylinder breakwater is a new type of environment-friendly breakwater, and the research on its wave absorbing characteristics is of great engineering significance. With the development of artificial intelligence, solving the water dynamics problem of breakwater based on machine learning technology has become a new research paradigm. This paper proposes a Convolutional Neural Network (CNN) model based on Sparrow Search Algorithm (SSA) to achieve intelligent optimization prediction of transmission coefficient of double-row perforated cylindrical breakwater. The results show that: (1) wave height, wave period, wavelength, wave velocity, row spacing, hole rate and water depth are identified as the key factors affecting the transmission coefficient. (2) When the population size of the SSA-CNN model is 10, the R2 value of the wave transmission coefficient prediction reaches 0.9909, and the average relative error is reduced by 22.24% compared with the single CNN model. The research results provide a new optimal prediction model for the study of wave transmission by using neural networks.

double-row perforated cylinder breakwater  /  wave absorbing characteristic  /  sparrow search algorithm  /  convolutional neural network  /  transmission coefficient
Bin Deng, Ling Wang, Jun He, Longbin Yin, Changbo Jiang, Jie Chen, Zhiyuan Wu. Prediction of transmission coefficient of double-row perforated cylinder breakwater based on SSA-CNN model[J]. Haiyang Xuebao, 2024 , 46 (4) : 122 -132 . DOI: 10.12284/hyxb2024035
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Article Info
doi: 10.12284/hyxb2024035
  • Receive Date：2024-01-11
  • Online Date：2025-11-26
  • Published：2024-04-30
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History
  • Received：2024-01-11
  • Revised：2024-03-21
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Affiliations
    1. School of Hydraulic and Environmental Engineering, Changsha University of Science & Technology, Changsha 410114, China
    2. Key Laboratory of Water-Sediment Sciences and Water Disaster Prevention of Hunan Province, Changsha 410114, China
    3. CCCC Water Transportation Consultants Co., Ltd., Beijing 100007, China
    4. State Key Laboratory of Hydraulic Engineering Intelligent Construction and Operation, Tianjin 300072, 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
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
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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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