基于综合智能分析的广东省崩塌易发性评价

doi:10.13722/j.cnki.jrme.2022.1204

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Abstract This study aims to improve the evaluation accuracy of collapse susceptibility in Guangdong Province using an intelligent analysis model. To this end，7 171 collapse data and 13 basic environmental factors in the province are collected，and the problem of massive data processing is solved by batch and block methods. Random Forest model(RF) and the coupled Frequency Ratio-Random Forest model(FR–RF) are used to carry out 1∶50 000 accuracy evaluation of collapse susceptibility. The performance of the model is evaluated by the receiver operating curve and the susceptibility distribution characteristics. The evaluation results were compared with those of traditional evaluation methods(Analytic Hierarchy Process(AHP)，Frequency Ratio(FR) and Coupled Frequency Ratio-Analytic Hierarchy Process(FR–AHP)). The results show that：(1) The 13 basic environmental factors have no significant correlation，and they all have a high degree of contribution. Topographic relief，elevation and annual average rainfall are the most important environmental factors affecting the occurrence of collapses in Guangdong Province；(2) The very high and high susceptibility areas are mainly distributed in the northeast，central and southwestern regions；(3) Among the five models，the prediction accuracy of RF is the highest，with an AUC value of 0.884，followed by FR–RF(0.860)，FR–AHP(0.797)，FR(0.794) and AHP (0.601). The uncertainty of the susceptibility index of RF and FR–RF is low. Except for AHP，all four models can be used to obtain reasonable evaluation results of collapse susceptibility，and RF has been found to be the most suitable method to evaluate the collapse susceptibility of Guangdong Province. Overall，the performance of the intelligent analysis method is better than that of the traditional evaluation method. This study can be considered as a reference for the evaluation of collapse susceptibility in large-scale low-altitude areas.

Key words： slope engineering evaluation of collapse susceptibility analytical hierarchy process frequency ratio random forest provincial scale

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	Articles by authors
	LEI Chengming1
	LIU Chunyan2
	ZHANG Yunbin2
	CHENG Jianmei1
	ZHAO Ruirui1

Cite this article:

LEI Chengming1,LIU Chunyan2,ZHANG Yunbin2, et al. Evaluation of collapse susceptibility in Guangdong Province based on comprehensive intelligent analysis[J]. , 2023, 42(S2): 4248-4260.

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https://rockmech.whrsm.ac.cn/EN/10.13722/j.cnki.jrme.2022.1204 OR https://rockmech.whrsm.ac.cn/EN/Y2023/V42/IS2/4248

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