基于CF模型的斜坡地质灾害孕灾因子敏感性分析——以湖南省新宁县为例

doi:10.19388/j.zgdzdc.2023.04.13

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Abstract The main controlling factors of slope geological hazards and their influence degrees are different in different regions and geological backgrounds. Analyzing the sensitivity of hazard inducing factors is beneficial to improve the accuracy of slope geological hazards prediction. On the basis of data collection and field investigation, the authors in this research selected eight factors, including slope, elevation, profile curvature, stratum, geolo-gical time, slope structure, fault, river and normalized difference vegetation index (NDVI), as the inducing factors of slope geological hazards. The sensitivity analysis is conducted by the certainty factor (CF) model based on GIS platform, and the sensitivity index was used to analyze the sensitivity of each factor and divide the sensitivity zones. The results show that areas with the most prone to disasters included characteristics of slope ≥40°, elevation [600,700) m, profile curvature ≥0.6, Triassic and Permian, along slope, [0, 500) m from the fault, [0, 200) m from the river and NDVI between [0.233, 0.595). The sensitivity was divided into five sensitive zones, namely extremely high, high, medium, low and extremely low. At the same time, the disaster data from 2020 to 2021 were used to verify that 87.50% of the disasters were located in extremely high and high sensitive areas, verifying the rationality of this study. The research results lay a foundation for the next step of risk assessment, and provide a reference for disaster prevention and for other counties and districts in Hunan Province.

Key words： slope geological disaster hazards inducing factors CF model sensitivity analysis Xinning County

Received: 01 December 2022

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	Articles by authors
	LIU Zhongnan
	GAO Yinmei
	JIANG He
	HUANG Yongwei
	DING Huadong
	YAN Zhiqiang

Cite this article:

LIU Zhongnan,GAO Yinmei,JIANG He, et al. Sensitivity analysis of inducing factors of slope geological hazards based on CF Model: A case study of Xinning County in Hunan Province[J]. , 2023, 10(4): 107-115.

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http://journal25.magtechjournal.com/Jwk_dzdc/EN/10.19388/j.zgdzdc.2023.04.13 OR http://journal25.magtechjournal.com/Jwk_dzdc/EN/Y2023/V10/I4/107

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