Overtopping prediction for composite slope breakwater based on random forest method

Hu Yuanye; Wang Shoujun; Chen Songgui; Liu Ye; Wang Jiawei; Tian Yunyan

doi:10.12284/hyxb2021133

Volume 43 Issue 10

Oct. 2021

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Hu Yuanye，Wang Shoujun，Chen Songgui, et al. 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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Overtopping prediction for composite slope breakwater based on random forest method

doi: 10.12284/hyxb2021133

Hu Yuanye^{1, 2
,},
Wang Shoujun¹,
Chen Songgui^{2
,
,},
Liu Ye²,
Wang Jiawei^{1, 2},
Tian Yunyan^{1, 2}

1.
National Demonstration Center for Experimental Mechanical and Electrical Engineering Education, Tianjin University of Technology, Tianjin 300384, China
2.
National Engineering Laboratory for Port Hydraulic Construction Technology, Tianjin Research Institute for Transport Engineering, Tianjin 300456, China

Received Date: 2020-06-27
Rev Recd Date: 2021-04-09

Available Online: 2021-07-13

Publish Date: 2021-10-30

Abstract

Abstract

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

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References(22)

References

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