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Overtopping prediction for composite slope breakwater based on random forest method
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Yuanye Hu1, 2, Shoujun Wang1, Songgui Chen2, *, Ye Liu2, Jiawei Wang1, 2, Yunyan Tian1, 2
Haiyang Xuebao | 2021, 43(10) : 106 - 114
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Haiyang Xuebao | 2021, 43(10): 106-114
• Article •
Overtopping prediction for composite slope breakwater based on random forest method
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Yuanye Hu1, 2, Shoujun Wang1, Songgui Chen2, *, Ye Liu2, Jiawei Wang1, 2, Yunyan Tian1, 2
Affiliations
  • 1National Demonstration Center for Experimental Mechanical and Electrical Engineering Education, Tianjin University of Technology, Tianjin 300384, China
  • 2National Engineering Laboratory for Port Hydraulic Construction Technology, Tianjin Research Institute for Transport Engineering, Tianjin 300456, China
Published: 2021-10-25 doi: 10.12284/hyxb2021133
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Aiming at the problem of calculating overtopping of the composite slope breakwater, a prediction model of the overtopping for the composite slope based on the random forest method is proposed. Firstly, by filtering the European CLASH data set, the data consistent with the prediction of overtopping of the composite slope breakwater are selected. Secondly, after dimensionless processing of the data, overtopping prediction model is established based on random forest method, and improved by adjusting the model parameters according to GridSearchCV. Finally, the coefficient of determination R2 is used to evaluate the accuracy of the model, and the prediction ability of the model is compared with the ensemble neural network model. The effect of each feature parameter of the random forest model on the prediction accuracy is assessed. The results show that the coefficient of determination of the random forest model is 92.7%, and the coefficient of determination of the ensemble neural network model is 87.7%, indicating the random forest model has a stronger prediction ability for predicting overtopping. Wall height with respect to static water level has the greatest influence on the prediction accuracy of the model, the height of the top of the embankment is the second, and the width of the foot of the embankment least.

random forest  /  overtopping  /  composite slope breakwater  /  coefficient of determination  /  feature importance  /  prediction
Yuanye Hu, Shoujun Wang, Songgui Chen, Ye Liu, Jiawei Wang, Yunyan Tian. Overtopping prediction for composite slope breakwater based on random forest method[J]. Haiyang Xuebao, 2021 , 43 (10) : 106 -114 . DOI: 10.12284/hyxb2021133
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Article Info
doi: 10.12284/hyxb2021133
  • Receive Date：2020-06-27
  • Online Date：2026-02-26
  • Published：2021-10-25
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  • Received：2020-06-27
  • Revised：2021-04-09
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Affiliations
    1National Demonstration Center for Experimental Mechanical and Electrical Engineering Education, Tianjin University of Technology, Tianjin 300384, China
    2National Engineering Laboratory for Port Hydraulic Construction Technology, Tianjin Research Institute for Transport Engineering, Tianjin 300456, 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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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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