ISSN 1003-8035 CN 11-2852/P
    屠水云,张钟远,付弘流,等. 基于CF与CF-LR模型的地质灾害易发性评价[J]. 中国地质灾害与防治学报,2022,33(2): 96-104. DOI: 10.16031/j.cnki.issn.1003-8035.2022.02-12
    引用本文: 屠水云,张钟远,付弘流,等. 基于CF与CF-LR模型的地质灾害易发性评价[J]. 中国地质灾害与防治学报,2022,33(2): 96-104. DOI: 10.16031/j.cnki.issn.1003-8035.2022.02-12
    TU Shuiyun, ZHANG Zhongyuan, FU Hongliu, et al. Geological hazard susceptibility evaluation based on CF and CF-LR model[J]. The Chinese Journal of Geological Hazard and Control, 2022, 33(2): 96-104. DOI: 10.16031/j.cnki.issn.1003-8035.2022.02-12
    Citation: TU Shuiyun, ZHANG Zhongyuan, FU Hongliu, et al. Geological hazard susceptibility evaluation based on CF and CF-LR model[J]. The Chinese Journal of Geological Hazard and Control, 2022, 33(2): 96-104. DOI: 10.16031/j.cnki.issn.1003-8035.2022.02-12

    基于CF与CF-LR模型的地质灾害易发性评价

    Geological hazard susceptibility evaluation based on CF and CF-LR model

    • 摘要: 区域地质灾害易发性评价对地质灾害防治具有重要意义。本文以贵州省沿河县为研究区,考虑海拔、坡度、坡向、地形曲率、NDVI、工程地质岩组、断层、道路、水系9个因素,通过相关性分析后作为评价因子。分别利用CF模型和CF-LR模型评价沿河县地质灾害易发性。结果表明:CF模型比CF-LR模型地质灾害易发性等级的频率比值从低易发区到极高易发区明显增大,均有效评价了沿河县地质灾害易发性;CF-LR模型比CF模型AUC值提高了0.096,CF-LR模型具有更高的评价精度。

       

      Abstract: The evaluation of regional geological disaster susceptibility is of great significance to the prevention and control of geological disasters. This paper takes Yanhe County in Guizhou Province as the research area, and considers 9 factors including altitude, slope, aspect, terrain curvature, NDVI, engineering geological rock formations, faults, roads, and water systems as evaluation factors. The CF model and the CF-LR model were used to evaluate the susceptibility of geological disasters in Yanhe County. The results show that the frequency ratio between the CF model and the CF-LR model of geological hazard susceptibility levels increases significantly from low-prone areas to extremely high-prone areas, which effectively evaluates the susceptibility of geological hazards in Yanhe County; the CF-LR model compares The AUC value of the CF model is increased by 0.096, and the CF-LR model has a higher evaluation accuracy.

       

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