Volume 28 Issue S1
Oct.  2020
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CHE Wenchao, QIN Shengwu, MIAO Qiang, SU Gang, CHEN Yang, YAO Jingyu. 2020: RESEARCH ON FACTOR CLASSIFICATION METHOD OF LANDSLIDE SUSCEPTIBILITY MAPPING. JOURNAL OF ENGINEERING GEOLOGY, 28(S1): 116-124. doi: 10.13544/j.cnki.jeg.2020-293
Citation: CHE Wenchao, QIN Shengwu, MIAO Qiang, SU Gang, CHEN Yang, YAO Jingyu. 2020: RESEARCH ON FACTOR CLASSIFICATION METHOD OF LANDSLIDE SUSCEPTIBILITY MAPPING. JOURNAL OF ENGINEERING GEOLOGY, 28(S1): 116-124. doi: 10.13544/j.cnki.jeg.2020-293

RESEARCH ON FACTOR CLASSIFICATION METHOD OF LANDSLIDE SUSCEPTIBILITY MAPPING

doi: 10.13544/j.cnki.jeg.2020-293
Funds:

This research is supported by the National Natural Science Foundation of China(Grant No. 41977221)

  • Received Date: 2020-06-28
  • Rev Recd Date: 2020-07-24
  • Taking Yanzi River Basin as an example, this paper studied the effect of influencing factor classification results on landslide susceptibility mapping. According to the historical landslides data and geological environment characteristics, this paper used elevation, slope, aspect, plane curvature, profile curvature, stratigraphic lithology, distance to faults, distance to roads, normalized difference vegetation index, topographic wetness index and standardized precipitation index as the influencing factors. With different classification data, the classification results are different, which will produce different susceptibility maps. Therefore, for continuous factor classifica ̄tion, this paper used the landslide points and all points in the study area as the classification data, respectively. The optimal classification number of continuous impact factors was determined by the inflection point method, and the corresponding classification interval was determined by Fisher-Jenks algorithm. Discrete impact factors were classified according to the actual situation. After influencing factor classification, support vector machine method was used to construct susceptibility mapping models, and the receiver operating characteristic(ROC)curve was used to evaluate the model performance. The results show the inflection point method and Fisher-Jenks algorithm can be well applied to the classification of continuous influencing factors. Compared with using all points in the entire study area to determine the factor classification criteria, the classification effect of using landslide points is better and the susceptibility map performs better. The areas under ROC curve corresponding to the two methods are 0.82(using all points in the study area as classification data) and 0.87(using all landslides points as classification data),respectively. The susceptibility maps of the Yanzi River Basin drawn in this paper can be used as a reference for disaster prevention and reduction.
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  • Aril A,Tetsuya K,Yoshinori S. 2018. Comparison of GIS-based landslide susceptibility models using frequency ratio, logistic regression, and artificial neural network in a tertiary region of Ambon, Indonesia[J]. Geomorphology,318:101-111.
    Coles A R,Quintero-Angel M. 2018. From silence to resilience:prospects and limitations for incorporating non-expert knowledge into hazard management[J]. Taylor & Francis,17(2):128-145.
    Guo J H,Liu C Q,Liu C. 2017. The Construction of standard driving cycle based on genetic algorithm optimization[J]. Science Technology and Engineering,17(15):327-333.
    Huang R Q,Li W L. 2008. Study on the development and distribution of geological disasters triggered by the "5.12" Wenchuan Earthquake[J]. Chinese Journal of Rock Mechanics and Engineering,27(12):2585-2592.
    Hu X Y,Qin S W,Dou Q,et al. 2019. Susceptibility analysis of debris flow based on GIS and random forest——a case study of a mountainous area in northern taonan city, Jilin province[J]. Bulletin of Soil and Water Conservation,39(5):204-210.
    Li N Q,Xu G Y. 2020. Grid analysis of land use based on natural breaks(jenks) classification[J]. Bulletin of Surveying and Mapping,(4):106-110.
    Liu F,Qin S W,Qiao S S,et al. 2019. Slope geological hazards susceptibility evaluation based on neural network model:a case study from Yongji county of Jilin province[J]. Global Geology,38(4):1166-1176.
    Luo L G,Pei X J,Huang R Q,et al. 2020. Landslide susceptibility assessment by GIS based on certainty factor and logistic regression model in Jiuzhaigou scenic area[J]. Journal of Engineering Geology, https://doi.org/10.13544/j.cnki.jeg.2019-202.
    Platt J C. 2000. Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods[J]. Advances in Large Margin Classifiers,10(4):61-74.
    Tan L,Chen G,Wang S Y,et al. 2014. Landslide susceptibility mapping based on logistic regression and support vector machine[J]. Journal of Engineering Geology,22(1):56-63.
    Wang N Q,Guo Y J,Liu T M,et al. 2019. Landslide susceptibility assessment based on support vector machine model[J]. Science Technology and Engineering,19(35):70-78.
    Xu Y Z,Lu Y N,Li D Y,et al. 2106. GIS and information model based landslide susceptibility assessment in granite area of Guangxi province[J]. Journal of Engineering Geology,24(4):693-703.
    Yang G,Xu P H,Cao C,et al. 2019. Assessment of regional landslide susceptibility based on combined model of certainty factor method[J]. Journal of Engineering Geology,27(5):1153-1163.
    Yang Q,Ye Z N,Gao Y L,et al. 2018. Dataset of geological disaster survey in Yanzi River Basin, upper reaches of Jialing River in 2015[J]. Geology in China,45 (S2):47-55.
    Zêzere J L,Pereira S,Melo R,et al. 2017. Mapping landslide susceptibility using data-driven methods[J]. Science of The Total Environment,589:250-267.
    Zhang Q K,Ling S Y,Li X N,et al. 2020. Comparison of landslide susceptibility mapping rapid assessments in Jiuzhaigou county, Sichuan province, China[J]. Chinese Journal of Rock Mechanics and Engineering, https://doi.org/10.13722/j.cnki.jrme.2020.0029.
    Zhang W,Bai S B,Wang J. 2010. Regional landslide susceptibility assessments based expert experience——a case study of Gaopingpu reservoir area, Pingwu county, Sichuan province[J]. Journal of Geological Hazards and Environment Preservation,21 (4):20-23,37.
    Zhang Y X,Lan H X,Li L P,et al. 2019. Combining statistical model and physical model for refined assessment of geological disaster——a case study of Longshan community in Fujian province[J]. Journal of Engineering Geology,27(3):608-622.
    Zhu A X,Pei T,Qiao J P,et al. 2006. A landslide susceptibility mapping approach using expert knowledge and fuzzy logic under GIS[J]. Progress in Geography,25 (4):1-12, 137.
    Zhu C H,Zhang J J,Ma D H,et al. 2020. Comprehensive analysis to the risk of landslides in the post-earthquake area based on DinSAR-BP neural networks[J]. Journal of Engineering Geology, https://doi.org/10.13544/j.cnki.jeg.2019-132.
    郭建华,刘初群,刘翠. 2017. 基于遗传算法优化的城市标准循环工况构建[J]. 科学技术与工程,17(15):327-333.
    黄润秋,李为乐. 2008. "5·12"汶川大地震触发地质灾害的发育分布规律研究[J]. 岩石力学与工程学报,27(12):2585-2592.
    扈秀宇,秦胜伍,窦强,等. 2019. 基于GIS和随机森林模型的泥石流敏感性分析——以吉林省洮南市北部山区为例[J]. 水土保持通报,39(5):204-210.
    李乃强,徐贵阳. 2020. 基于自然间断点分级法的土地利用数据网格化分析[J]. 测绘通报,(4):106-110.
    刘飞,秦胜伍,乔双双,等. 2019. 基于神经网络模型的斜坡地质灾害易发性评价:以吉林永吉为例[J]. 世界地质,38(4):1166-1176.
    罗路广,裴向军,黄润秋,等. 2020. GIS支持下CF与Logistic回归模型耦合的九寨沟景区滑坡易发性评价[J]. 工程地质学报,https://doi.org/10.13544/j.cnki.jeg.2019

    -202.
    谭龙,陈冠,王思源,等. 2014. 逻辑回归与支持向量机模型在滑坡敏感性评价中的应用[J]. 工程地质学报,22(1):56-63.
    王念秦,郭有金,刘铁铭,等. 2019. 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,19(35):70-78.
    许英姿,卢玉南,李东阳,等. 2106. 基于GIS和信息量模型的广西花岗岩分布区滑坡易发性评价[J]. 工程地质学报,24(4):693-703.
    杨光,徐佩华,曹琛,等. 2019. 基于确定性系数组合模型的区域滑坡敏感性评价[J]. 工程地质学报,27(5):1153-1163.
    杨强,叶振南,高幼龙,等. 2018.2015年嘉陵江上游燕子河流域地质灾害调查数据集[J]. 中国地质,45 (S2):47-55.
    张玘恺,凌斯祥,李晓宁,等. 2020. 九寨沟县滑坡灾害易发性快速评估模型对比研究[J]. 岩石力学与工程学报,https://doi.org/10.13722/j.cnki.jrme.2020.

    0029.
    张文,白世彪,王建. 2010. 基于专家经验值的滑坡易发性评价——以四川平武高坪铺库区为例[J]. 地质灾害与环境保护,21 (4):20-23,37.
    仉义星,兰恒星,李郎平,等. 2019. 综合统计模型和物理模型的地质灾害精细评估——以福建省龙山社区为例[J]. 工程地质学报,27(3):608-622.
    朱阿兴,裴韬,乔建平,等. 2006. 基于专家知识的滑坡危险性模糊评估方法[J]. 地理科学进展,25 (4):1-12,137.
    朱崇浩,张建经,马东华,等. 2020. 基于DinSAR-BP神经网络的震后区域滑坡危险性综合评价研究[J]. 工程地质学报,https://doi.org/10.13544/j.cnki.jeg.2019

    -132.
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