基于粗糙集–神经网络的IBURI地震滑坡易发性研究

doi:10.13722/j.cnki.jrme.2020.1122

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Abstract The landslides induced by strong earthquakes are wide in distribution and large in quantity，showing obvious regional characteristic. Besides intense ground motion，earthquake-triggered landslides are also affected by environmental conditions such as rainfall，regional hydrogeology and other complex factors. Reasonable index selection strategy can fundamentally improve the accuracy and efficiency of the susceptibility evaluation of seismic landslides. The present research takes 3307 landslides triggered by Mj 6.7 Hokkaido Eastern Iburi earthquake on September 6，2018，as the study object to analyze the construction of evaluation indicator system. First，based on the comprehensive consideration of seismic characteristics，17 original evaluation indicators are selected according to the earthquake disaster investigation and data analysis. Then，rough set theory is introduced as the index selection strategy，and the reduction ability of the rough set is adopted to delete 9 factors with less relevance to landslides. Finally，the input layer of BP neural network is constructed with the optimized indicator system，and a rough set-BP neural network model for evaluating the susceptibility of seismic landslides is established. The results show that the prediction accuracy of the model is improved from 63.8% to 94.4%，indicating that the rough set-BP neural network model can effectively improve the accuracy of seismic landslide susceptibility evaluation.

Key words： slope engineering seismic landslides evaluation of susceptibility rough set theory BP neural network Hokkaido IBURI earthquake

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	Articles by authors
	WU Yuchen
	ZHOU Hanxu
	CHE Ailan

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WU Yuchen,ZHOU Hanxu,CHE Ailan. Susceptibility of landslides caused by IBURI earthquake based on rough set-neural network[J]. , 2021, 40(6): 1226-1235.

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http://rockmech.whrsm.ac.cn/EN/10.13722/j.cnki.jrme.2020.1122 OR http://rockmech.whrsm.ac.cn/EN/Y2021/V40/I6/1226

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