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MA Xiao, WANG Nianqin, LI Xiaokang, et al. Assessment of Landslide Susceptibility Based on RF-FR Model: Taking Lueyang County as an Example[J]. Northwestern Geology, 2022, 55(3): 335-344. DOI: 10.19751/j.cnki.61-1149/p.2022.03.028
Citation: MA Xiao, WANG Nianqin, LI Xiaokang, et al. Assessment of Landslide Susceptibility Based on RF-FR Model: Taking Lueyang County as an Example[J]. Northwestern Geology, 2022, 55(3): 335-344. DOI: 10.19751/j.cnki.61-1149/p.2022.03.028

Assessment of Landslide Susceptibility Based on RF-FR Model: Taking Lueyang County as an Example

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  • Received Date: July 11, 2021
  • Revised Date: January 10, 2022
  • Available Online: August 25, 2022
  • The assessment of landslide susceptibility is an important means to guide the preliminary early warning and forecast of regional landslide. In order to improve the accuracy of landslide susceptibility evaluation in county area, random forest model (RF) and frequency ratio model (FR) were used as the basic models, and RF-FR was established to evaluate landslide susceptibility combined with the advantages of the two models. In Lueyang county domain for the study area, selection of elevation, slope direction, slope, formation, surface roughness, the distance from the fault, curvature and the distance from the road, terrain humidity index, the distance from the river, a database of 14 factors such as rainfall, by adopting the method of Spearman correlation analysis of each factor, eliminate topographic relief degree of three high correlation of evaluation factors, and evaluate the relative point density (LRPD) based on landslide factor analysis. The results show that:①there is a negative correlation between the distance between landslide disaster points and linear factors, that is, the closer the distance is, the more disaster points are. ②The prediction rates of FR, RF and RF-FR models are 84.3%, 90. 1% and 95.0%, respectively. Compared with FR and RF models, the prediction accuracy of RF-FR model is 10.7% and 4.9% higher than that of FR and RF models. ③The proportion of landslide disaster points in high and extremely high-risk areas of 4RFmurFR model is 15.89% and 5.29% higher than that of FR and RF model, respectively.
  • 孙果梅, 况明生, 曲华.陕西秦巴山区地质灾害研究[J].水土保持研究, 2005, 21(05):244-247.
    SUN Guomei, KUANG Mingsheng, QU Hua.Reserch of Geological Disaster in Qinling-Bashan Mountains[J].Reserch of Soil and Water Conservation, 2005, 21(05):244-247.
    殷坤龙, 张桂荣.地质灾害风险区划与综合防治对策[J].安全与环境工程, 2003, 10(01):32-35.
    YIN Kunlong, ZHANG Guirong. Risk Zonation of Geo-hazards and Its Comprehensive Control[J].Safety and Environmental Engineering, 2003, 10(01):32-35.
    王高峰, 郭宁, 邓兵, 等.不同组合模型区域滑坡易发性及精度分析[J].西北地质, 2021, 54(02):259-272.
    WANG Gaofeng, GUO Ning, DENG Bing, et al.Analysis of Landslide Susceptility and Accuracy in Different Combination Models[J].Northwestern Geology, 2021, 54(02):259-272.
    胡涛, 樊鑫, 王硕, 等.基于逻辑回归模型和3S技术的思南县滑坡易发性评价[J].地质科技通报, 2020, 39(02):113-121.
    HU Tao, FAN Xin, WANG Shuo, et al. Landslide Susceptility Evaluation of Geological Sinan County Using Logistics Regression Model and 3Stechology[J] Bulletin of Geological Science and Technology, 2020, 39(02):113-121.
    许冲, 戴福初, 姚鑫, 等.GIS支持下基于层次分析法的汶川地震区滑坡易发性评价[J].岩石力学与工程学报, 2009, 28(S2):3978-3985.
    XU Chong, DAI Fuchu, YAO Xin, et al.GIS-Based Landslide Susceptibility Assessment Using Analytical Hierarchy Process in Wenchuan Earthquake Region[J].Chinese Journal of Rock Mechanics and Engineering, 2009, 28(S2):3978-3985.
    高克昌, 崔鹏, 赵纯勇, 等.基于地理信息系统和信息量模型的滑坡危险性评价——以重庆万州为例[J].岩石力学与工程学报, 2006, 25(05):991-996.
    GAO Kechang, CUI Peng, ZHAO Chunyong, et al. Langslide Hazard Evaluation of Wanzhou Based on GIS Information Value Method in the Three Gorges Reservoir[J].Chinese Journal of Rock Mechanics and Engineering, 2006, 25(05):991-996.
    齐信, 黄波林, 刘广宁, 等.基于GIS技术和频率比模型的三峡地区秭归向斜盆地滑坡敏感性评价[J]. 地质力学学报, 2017, 23(01):97-104.
    QI Xin, HUANG Bolin, LIU Guangning, et al. Landslide Susceptibility Assessment in The Three Gorges Area, China, Zigui Synclinal Basin, Using GIS Technology and Frequency Ratio Model[J].Journal of Geomechanics, 2017, 23(01):97-104.
    刘月, 王宁涛, 周超, 等.基于ROC曲线与确定性系数法集成模型的三峡库区奉节县滑坡易发性评价[J].安全与环境工程, 2020, 27(04):61-70.
    LIU Yue, WANG Ningtao, ZHOU Chao, et al. Evaluation of Landslide Susceptibility Based on ROC and Certainty Factor Method in Fengjie County, Three Gorges Reservoir[J].Safety and Environmental Engineering, 2020, 27(04):61-70.
    王念秦, 郭有金, 刘铁铭, 等.基于支持向量机模型的滑坡危险性评价[J].科学技术与工程, 2019, 19(35):70-78.
    WANG Nianqin, GUO Youjin, LIU Tieming, et al. Landslide susceptibility assessment based on support vector machine model[J]. Science Technology and Engineering, 2019, 19(35):70-78.
    郭子正, 殷坤龙, 黄发明, 等.基于滑坡分类和加权频率比模型的滑坡易发性评价[J].岩石力学与工程学报, 2019, 38(02):287-300.
    GUO Zizheng, YIN Shenlong, HUANG Faming, et al. Evaluation of Landslide Susceptibility Based on Landslide Classification and Weighted Frequency Ratio Model[J].Chinese Journal of Rock Mechanics and Engineering, 2019, 38(02):287-300.
    邱维蓉, 吴帮玉, 潘学树, 等.几种聚类优化的机器学习方法在灵台县滑坡易发性评价中的应用[J].西北地质, 2020, 53(01):222-233.
    QIU Weirong, WU Bangyu, PAN Xueshu, et al.Application of Several Cluster-optimization-based Machine Learning Methodsin Evaluation of Landslide Susceptibility in Lingtai County[J].Northwestern Geology, 2020, 53(01):222-233.
    吴常润, 赵冬梅, 刘澄静, 等.基于GIS和信息量模型的陇川县滑坡易发性评价[J].西北地质, 2020, 53(02):308-320.
    WU Changrun, ZHAO Dongmei, LIU Chengjing, et al.Landslide Susceptibility Assessment of Longchuan County Based on GIS and Information Value Model[J].Northwestern Geology, 2020, 53(02):308-320.
    Cheng Wei, Xie Xiaoshen, Peng Jianbing, et al.GIS-based landslide susceptibility evaluation using anovel hybrid integration approach of bivariate statisticalbased random forestmethod[J].Catena, 2018, 164(01):135-14.
    Cao J, Zhang Z, Wang C Z, etal. Susceptibility assessment of landslides triggered by earthquakes in the Western Sichuan Plateau[J]. Catena, 2019, 175(02):63-76.
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