基于LR-Ⅳ模型的滑坡敏感性评价
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摘要
滑坡敏感性评价是滑坡防治中的重要一环.针对信息量模型(Ⅳ)中各影响因子等权以及逻辑回归模型(LR)中因子难以量化的问题,将逻辑回归模型(LR)与信息量模型(Ⅳ)进行耦合,构建LR-Ⅳ模型,并将逻辑回归模型及信息量模型作为对照,以湖北省为例,进行了滑坡敏感性评价.选取工程岩组、地震烈度、水系距离、构造线距离、地貌、降雨面、坡度等七个因子,结合GIS的栅格分析方法,计算各区域滑坡敏感性指数,得到湖北省滑坡敏感性区划图.将历史滑坡数据随机分为70%的训练数据与30%的验证数据,通过受试者工作特征曲线来比较各个模型之间的性能.在成功率上,LR-Ⅳ模型(91.3%)与逻辑回归模型(91.5%)、信息量模型(90.3%)相差不大,都具有极高的成功率,但在预测率上,LR-Ⅳ模型(75.7%)远胜于逻辑回归模型(65.8%),略强于信息量模型(74.9%).研究结果表明,LR-Ⅳ模型表现优异,可用于滑坡敏感性评价.
Landslide susceptibility assessment is an important part of landslide prevention and control. In order to solve the problem that the factors in the Information Value Model(Ⅳ)have equal weights,and the factors in the logistic regression model(LR)are difficult to quantify,this study constructs LR-Ⅳ model by coupling logistic regression model with information value model. The logistic regression model and information value model were taken as a control. With Hubei Province as an example,landslide susceptibility assessment was conducted. In this study,we selected seven factors of engineering rock group,seismic intensity,distance of water system,distance of structural line,topography,rainfall surface,slope and so on. Combined with the grid analysis method of GIS,the landslide susceptibility index of each area is calculated,and the zoning map of landslide susceptibility in Hubei Province was finally obtained. During the model comparison,historical landslide data were randomly divided into 70% of training data and 30% of validation data,and the performance of each model was compared by the receiver operating characteristic curve. The LR-Ⅳ model(91.3%)has a very high success rate compared with the logistic regression model(91.5%)and the information value model(90.3%),but the LR-Ⅳ model(75.7%)is far superior to the logistic regression model(65.8%)and slightly stronger than the information value model(74.9%). Overall,the LR-Ⅳ model performswell and can be used for the landslide susceptibility assessment.
Landslide susceptibility assessment is an important part of landslide prevention and control. In order to solve the problem that the factors in the Information Value Model(Ⅳ)have equal weights,and the factors in the logistic regression model(LR)are difficult to quantify,this study constructs LR-Ⅳ model by coupling logistic regression model with information value model. The logistic regression model and information value model were taken as a control. With Hubei Province as an example,landslide susceptibility assessment was conducted. In this study,we selected seven factors of engineering rock group,seismic intensity,distance of water system,distance of structural line,topography,rainfall surface,slope and so on. Combined with the grid analysis method of GIS,the landslide susceptibility index of each area is calculated,and the zoning map of landslide susceptibility in Hubei Province was finally obtained. During the model comparison,historical landslide data were randomly divided into 70% of training data and 30% of validation data,and the performance of each model was compared by the receiver operating characteristic curve. The LR-Ⅳ model(91.3%)has a very high success rate compared with the logistic regression model(91.5%)and the information value model(90.3%),but the LR-Ⅳ model(75.7%)is far superior to the logistic regression model(65.8%)and slightly stronger than the information value model(74.9%). Overall,the LR-Ⅳ model performswell and can be used for the landslide susceptibility assessment.
引文
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[6]李家存,李京,Jasmi.基于概率指数模型的区域滑坡危险性评价[J].中国图像图形学报,2007,12(8):1471-1475.
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[10]刘丽娜,许冲,徐锡伟,等. GIS支持下基于AHP方法的2013年芦山地震区滑坡危险性评价[J].灾害学,2014,29(4):183-191.
[11]冯杭建,周爱国,俞剑君,等.浙西梅雨滑坡易发性评价模型对比[J].地球科学,2016,41(3):403-415.
[12]牛全福,冯尊斌,张映雪,等.基于GIS的兰州地区滑坡灾害孕灾环境敏感性评价[J].灾害学,2017,32(3):29-35.
[13]谭龙,陈冠,王思源,等.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,2014,22(1):56-63.
[14]刘洋,唐川,冯毅.基于AHP-信息量法的地质灾害危险性评价[J].地球与环境,2013,41(2):173-179.
[15]孙冉,王成都,夏哲兵,等.基于AHP-信息量法的费县地质灾害风险评价[J].环境科学与技术,2015,38(S1):430-435.
[16]詹华勤.基于GIS川黔铁路贵州段地质灾害易发性评价研究[D].成都:西南交通大学地球科学与环境工程学院,2017:59-64.
[17]冯非凡,武雪玲,牛瑞卿,等.粒子群优化BP神经网络的滑坡敏感性评价[J].测绘科学,2017,42(10):170-175.
[18] JAAFARI A,NAJAFI A,POURGHASEMI H R,et al. GIS-based frequency ratio and index of entropy models for landslide susceptibility assessment in the Caspian forest,northern Iran[J]. International Journal of Environmental Science and Technology,2014,11(4):909-926.
[19] AKGUN A. A comparison of landslide susceptibility maps produced by logistic regression,multi-criteria decision,and likelihood ratio methods:a case study at?zmir,Turkey[J]. Landslides,2012,9(1):93-106.
[20] POURGHASEMI H,PRADHAN B,GOKCEOGLU C. Application of fuzzy logic and analytical hierarchy process(AHP)to landslide susceptibility mapping at Haraz watershed,Iran[J]. Natural Hazards,2012,63(2):965-996.
[21] INTARAWICHIAN N,DASANANDA S. Frequency ratio model based landslide susceptibility mapping in lower Mae Chaem watershed,northern Thailand[J]. Environmental Earth Sciences,2011,64(8):2271-2285.
[22]冯杭建,唐小明,周爱国.浙江省泥石流与降雨历时关系研究及应用检验[J].自然灾害学报,2013,22(1):159-168.
[2] BRABB E E. The World landslide problem[J]. Episodes,1991,14(1):52-61.
[3] PRADHAN B. A comparative study on the predictive ability of the decision tree,support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS[J]. Computers and Geosciences,2013,51:350-365.
[4]殷坤龙,朱良峰.滑坡灾害空间区划及GIS应用研究[J].地学前缘,2001,8(2):279-284.
[5]牛全福,程维明,兰恒星,等.基于信息量模型的玉树地震次生地质灾害危险性评价[J].山地学报,2011,29(2):243-249.
[6]李家存,李京,Jasmi.基于概率指数模型的区域滑坡危险性评价[J].中国图像图形学报,2007,12(8):1471-1475.
[7]戴福初,姚鑫,谭国焕.滑坡灾害空间预测支持向量机模型及其应用[J].地学前缘,2007,14(6):153-159.
[8] MARJANOVI?M,KOVA?EVI?M,BAJAT B,et al. Landslide susceptibility assessment using SVM machine learning algorithm[J]. Engineering Geology,2011,123(3):225-234.
[9]许冲,徐锡伟.逻辑回归模型在玉树地震滑坡危险性评价中的应用与检验[J].工程地质学报,2012,20(3):326-333.
[10]刘丽娜,许冲,徐锡伟,等. GIS支持下基于AHP方法的2013年芦山地震区滑坡危险性评价[J].灾害学,2014,29(4):183-191.
[11]冯杭建,周爱国,俞剑君,等.浙西梅雨滑坡易发性评价模型对比[J].地球科学,2016,41(3):403-415.
[12]牛全福,冯尊斌,张映雪,等.基于GIS的兰州地区滑坡灾害孕灾环境敏感性评价[J].灾害学,2017,32(3):29-35.
[13]谭龙,陈冠,王思源,等.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,2014,22(1):56-63.
[14]刘洋,唐川,冯毅.基于AHP-信息量法的地质灾害危险性评价[J].地球与环境,2013,41(2):173-179.
[15]孙冉,王成都,夏哲兵,等.基于AHP-信息量法的费县地质灾害风险评价[J].环境科学与技术,2015,38(S1):430-435.
[16]詹华勤.基于GIS川黔铁路贵州段地质灾害易发性评价研究[D].成都:西南交通大学地球科学与环境工程学院,2017:59-64.
[17]冯非凡,武雪玲,牛瑞卿,等.粒子群优化BP神经网络的滑坡敏感性评价[J].测绘科学,2017,42(10):170-175.
[18] JAAFARI A,NAJAFI A,POURGHASEMI H R,et al. GIS-based frequency ratio and index of entropy models for landslide susceptibility assessment in the Caspian forest,northern Iran[J]. International Journal of Environmental Science and Technology,2014,11(4):909-926.
[19] AKGUN A. A comparison of landslide susceptibility maps produced by logistic regression,multi-criteria decision,and likelihood ratio methods:a case study at?zmir,Turkey[J]. Landslides,2012,9(1):93-106.
[20] POURGHASEMI H,PRADHAN B,GOKCEOGLU C. Application of fuzzy logic and analytical hierarchy process(AHP)to landslide susceptibility mapping at Haraz watershed,Iran[J]. Natural Hazards,2012,63(2):965-996.
[21] INTARAWICHIAN N,DASANANDA S. Frequency ratio model based landslide susceptibility mapping in lower Mae Chaem watershed,northern Thailand[J]. Environmental Earth Sciences,2011,64(8):2271-2285.
[22]冯杭建,唐小明,周爱国.浙江省泥石流与降雨历时关系研究及应用检验[J].自然灾害学报,2013,22(1):159-168.
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