九寨沟地震滑坡易发性评价因子组合选取研究

doi:10.13722/j.cnki.jrme.2021.0198

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Abstract The regional landslide susceptibility assessment is the basis of landslide hazards and risk assessments. However，how to scientifically select landslide conditioning factors is still a weak part of the previous researches. 1022 identified landslides induced by the Jiuzhaigou earthquake in the Jiuzhaigou National Geopark on August 8，2017 were taken as the sample dataset. A total of 16 landslide causative factors including seismic，topographic，geological，hydrological parameters and human engineering activities were selected. Based on the collinearity test of these continuous conditioning factors，the logistic regression(LR) model was used to generate 30 landslide assessment models by combining different covariates under the slope units，and then the 10-fold cross-validation and receiver operating characteristics(ROC) curve were adopted to assess success rate and prediction accuracy of the models in R statistic software. The results show that the LR model has good applicability in the earthquake-induced landslides(EQILs) susceptibility assessment for the study area，and EQILs are mainly controlled and affected by seismic and topographic parameters. Once hydrological conditions and human engineering activities are excluded out，the performance of the models decreases within a small range. Moreover，compared to the traditional data sampling methods，the cross-validation approach can effectively reduce selection bias and prediction variance，and can better test the robustness of the models. This research may enrich the theoretical research content of EQILs regional assessment and provide references for rapid post-earthquake assessment and disaster prevention and mitigation for the Jiuzhaigou area.

Key words： slope engineering earthquake-induced landslides(EQILs) landslide susceptibility factor combined selection 10-fold cross-validation logistic regression model

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	Articles by authors
	LUO Luguang
	PEI Xiangjun
	CUI Shenghua
	HUANG Runqiu
	ZHU Ling
	HE Zhihao

Cite this article:

LUO Luguang,PEI Xiangjun,CUI Shenghua, et al. Combined selection of susceptibility assessment factors for Jiuzhaigou earthquake-induced landslides[J]. , 2021, 40(11): 2306-2319.

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https://rockmech.whrsm.ac.cn/EN/10.13722/j.cnki.jrme.2021.0198 OR https://rockmech.whrsm.ac.cn/EN/Y2021/V40/I11/2306

[1] VARNES D J. Landslide hazard zonation：a review of principles and practice[M]. Paris：United Nations Economic，Scientific and Cultural Organization，UNESCO 3，1984：63.
[2] BRABB E. Innovative approaches for landslide hazard evaluation[C]// Proceedings of IVth International Conference and Field Workshop in Landslides. Tokyo，Japan：Japan Landslide Society，1984：307–323.
[3] REICHENBACH P，ROSSI M，MALAMUD B D，et al. A review of statistically-based landslide susceptibility models[J]. Earth-Science Reviews，2018，180：60–91.
[4] LEE C T，HUANG C C，LEE J F，et al. Statistical approach to earthquake-induced landslide susceptibility[J]. Engineering Geology，2008，100(1/2)：43–58.
[5] BUI D T，TUAN T A，KLEMPE H，et al. Spatial prediction models for shallow landslide hazards：a comparative assessment of the efficiency of support vector machines，artificial neural networks，kernel logistic regression，and logistic tree[J]. Landslides，2016，13(2)：361–378.
[6] 中国科学院科技战略咨询研究院，中国科学院文献情报中心. 2020研究前沿[EB/OL]. http：// www. dsac.cn/News/Detail/28422，2020–11–13/ 2020–11–16.(Institutes of Science and Development，National Science Library，Chinese Academy of Sciences. Research Fronts Report 2020[EB/OL]. http：//www. dsac.cn/News/Detail/ 28422，2020–11–13/2020–11–16.(in Chinese))
[7] FAN X，SCARINGI G，XU Q，et al. Coseismic landslides triggered by the 8th August 2017 Ms 7.0 Jiuzhaigou earthquake(Sichuan，China)：factors controlling their spatial distribution and implications for the seismogenic blind fault identification[J]. Landslides，2018，15(5)：967–983.
[8] 马思远，许冲，田颖颖，等. 基于逻辑回归模型的九寨沟地震滑坡危险性评估[J]. 地震地质，2019，41(1)：162–177.(MA Siyuan，XU Chong，TIAN Yingying，et al. Application of logistic regression model for hazard assessment of earthquake-triggered landslides：a case study of 2017 Jiuzhaigou(China) MS 7.0 event[J]. Seismology and Geology，2019，41(1)：162–177.(in Chinese))
[9] 许冲，徐锡伟，周本刚，等. 同震滑坡发生概率研究——新一代地震滑坡危险性模型[J]. 工程地质学报，2019，27(5)：1 121–1 129. (XU Chong，XU Xiwei，ZHOU Bengang，et al. Probability of coseismic landslides：A new generation of earthquake-triggered landslide hazard model[J]. Journal of Engineering Geology，2019，27(5)：1 121–1 129. (in Chinese))
[10] 罗路广，裴向军，黄润秋. 强震山区地震滑坡发生概率研究——以九寨沟国家地质公园为例[J]. 岩石力学与工程学报，2020，39(10)：2 079–2 093.(LUO Luguang，PEI Xiangjun，HUANG Runqiu. Earthquake-triggered landslide occurrence probability in strong seismically mountainous areas：a case study of Jiuzhaigou National Geopark[J]. Chinese Journal of Rock Mechanics and Engineering，2020，39(10)：2 079–2 093.(in Chinese))
[11] YI Y，ZHANG Z，ZHANG W，et al. Landslide susceptibility mapping using multiscale sampling strategy and convolutional neural network：A case study in Jiuzhaigou region[J]. Catena，2020，195：104851.
[12] CAO J，ZHANG Z，DU J，et al. Multi-geohazards susceptibility mapping based on machine learning—a case study in Jiuzhaigou，China[J]. Natural Hazards，2020，102(3)：851–871.
[13] ALEOTTI P，CHOWDHURY R. Landslide hazard assessment：summary review and new perspectives[J]. Bulletin of Engineering Geology and the Environment，1999，58(1)：21–44.
[14] AMATO G，FIORUCCI M，MARTINO S，et al. Earthquake-triggered landslide susceptibility in Italy by means of Artificial Neural Network[EB/OL]. [2021–03–08]. https：//xueshu.baidu.com/usercenter/ paper/show?paperid=1x7a0a60x20m0tf0mv5d0c20rf749269&site=xueshu_se.
[15] PRADHAN B，LEE S. Landslide susceptibility assessment and factor effect analysis：backpropagation artificial neural networks and their comparison with frequency ratio and bivariate logistic regression modelling[J]. Environmental Modelling and Software，2010，25(6)：747–759.
[16] 黄发明，殷坤龙，蒋水华，等. 基于聚类分析和支持向量机的滑坡易发性评价[J]. 岩石力学与工程学报，2018，37(1)：156–167. (HUANG Faming，YIN Kunlong，JIANG Shuihua，et al. Landslide susceptibility assessment based on clustering analysis and support vector machine[J]. Chinese Journal of Rock Mechanics and Engineering，2018，37(1)：156–167.(in Chinese))
[17] HUANG F，YIN K，HUANG J，et al. Landslide susceptibility mapping based on self-organizing-map network and extreme learning machine[J]. Engineering Geology，2017，223：11–22.
[18] 李雪平，唐辉明. 贝叶斯信息标准在滑坡因子敏感性分析中的应用[J]. 岩土力学，2006，27(8)：1 393–1 397.(LI Xueping，TANG Huiming. Application of Bayesian information criterion to sensitivity analysis of landslide factor[J]. Rock and Soil Mechanics，2006，27(8)：1 393–1 397.(in Chinese))
[19] 赵忠国，张峰，郑江华. 多元自适应回归样条法的滑坡敏感性评价[J]. 武汉大学学报：信息科学版，2021，46(3)：442–450.(ZHAO Zhongguo，ZHANG Feng，ZHENG Jianghua. Evaluation of landslide susceptibility by multiple adaptive regression spline method[J]. Geomatics and Information Science of Wuhan University：2021，46(3)：442–450.(in Chinese))
[20] ROTIGLIANO E，CAPPADONIA C，CONOSCENTI C，et al. Slope units-based flow susceptibility model：using validation tests to select controlling factors[J]. Natural Hazards，2012，61(1)：143–153.
[21] COSTANZO D，ROTIGLIANO E，IRIGARAY C，et al. Factors selection in landslide susceptibility modelling on large scale following the gis matrix method：application to the river Beiro basin(Spain)[J]. Natural Hazards and Earth System Sciences，2012，12(2)：327–340.
[22] KAVZOGLU T，SAHIN E K，COLKESEN I. Selecting optimal conditioning factors in shallow translational landslide susceptibility mapping using genetic algorithm[J]. Engineering Geology，2015，192：101–112.
[23] 于宪煜，胡友健，牛瑞卿. 基于RS-SVM模型的滑坡易发性评价因子选择方法研究[J]. 地理与地理信息科学，2016，32(3)：23–28.(YU Xianyu，HU Youjian，NIU Ruiqing. Research on the method to select landslide susceptibility evaluation factors based on RS-SVM model[J]. Geography and Geo-Information Science，2016，32(3)：23–28.(in Chinese))
[24] KEEFER D K. Landslides caused by earthquakes[J]. Geological Society of America Bulletin，1984，95(4)：406–421.
[25] HOSMER D W，LEMESHOW S. Applied logistic regression[M]. 2nd ed. New York：Wiley，2000：160–164.
[26] 李秀珍，孔纪名，崔云，等. 汶川地震滑坡与地震参数及地质地貌因素之间的相关关系[J]. 工程地质学报，2010，18(1)：8–14.(LI Xiuzhen，KONG Jiming，CUI Yun，et al. Statistical relations between landslides induced by Wenchuan earthquake and earthquake parameters，geological as well as geomorphological factors[J]. Journal of Engineering Geology，2010，18(1)：8–14.(in Chinese))
[27] LOMBARDO L，SAIA S，SCHILLACI C，et al. Modeling soil organic carbon with quantile regression：Dissecting predictors' effects on carbon stocks[J]. Geoderma，2018，318：148–159.
[28] 周德培，钟卫，杨涛. 基于坡体结构的岩质边坡稳定性分析[J]. 岩石力学与工程学报，2008，27(4)：687–695.(ZHOU Depei，ZHONG Wei，YANG Tao. Stability analysis of rocky slope based on slope structures[J]. Chinese Journal of Rock Mechanics and Engineering，2008，27(4)：687–695.(in Chinese))
[29] MOORE D S，NOTZ W I. Statistics：concepts and controversies[M]. Macmillan：[s. n.]，2006：234–236.
[30] ALVIOLI M，MARCHESINI I，REICHENBACH P，et al. Automatic delineation of geomorphological slope units with r.slopeunits v1.0 and their optimization for landslide susceptibility modeling[J]. Geoscientific Model Development，2016，9(11)：3 975–3 991.
[31] GEISSER S. The predictive sample reuse method with applications[J]. Journal of the American statistical Association，1975，70(350)：320–328.
[32] 杨德旭. 云南省巧家县地震前后滑坡易发性评价及地震作用影响综合研究[硕士学位论文][D]. 昆明：昆明理工大学，2019.(YANG Dexu. Comprehensive study on landslide susceptibility evaluation and seismic effect before and after earthquake in Qiaojia County，Yunnan Province[M. S. Thesis][D]. Kunming：Kunming University of Science and Technology，2019.(in Chinese))