基于滑坡分类和加权频率比模型的滑坡易发性评价
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摘要
根据区域滑坡特点,针对不同类型滑坡的自身特征分别建立指标评价体系,能够使滑坡易发性评价的过程更加科学准确。以三峡库区万州区内滑坡为例,首先,基于对地质环境、滑坡空间分布及自身特征的分析,将全区滑坡分为陡倾角地层滑坡和缓倾角地层滑坡。其次,获取12种指标因子(高差、坡度、坡向、平面曲率、剖面曲率、地层岩性、水系、地质构造、公路、地层倾角、降雨、含蒙脱石软弱夹层厚度)构成基本评价体系。然后提出基于逻辑回归(logistic regression,LR)–模糊层次分析(fuzzy analytical hierarchy process,FAHP)方法(LR-FAHP)的加权频率比模型(weighted frequency ratio model,WFR),通过对指标因子的重要性进行排序,实现各指标因子权重的定量计算,从而建立不同类型滑坡的评价指标体系,再基于GIS平台实现全区滑坡灾害的易发性等级预测。结果表明:与单一的LR,FAHP和FR三种模型相比,WFR模型能将滑坡易发性评价精度提升4%～9%,表明LR-FAHP是一种定量计算指标因子权重的有效方法;同时,基于滑坡分类的WFR模型的易发性评价成功率为79.2%,预测率为79.6%,均优于未进行滑坡分类的WFR模型,为建立评价指标体系和区域滑坡易发性评价提供了可靠途径。
According to the regional landslide features,an evaluation index system developed based on the characteristics among different types of landslides,can make landslide susceptibility evaluation more scientific and accurate. Taking Wanzhou district of Three Gorges Reservoir as an example,the regional landslides were divided into steeply inclined strata landslides and gently inclined strata landslides based on geological situation,landslide spatial distribution and characteristics. Twelve index factors including elevation difference,slope angle,aspect,plane and profile curvatures,stratum lithology,water distribution,geological structure,highway,stratum dip,rainfall and weak interlayer containing montmorillonite were obtained to build the basic evaluation system,and a weighted frequency ratio model(WFR) was proposed to perform regional landslide susceptibility evaluation based on the logistic regression(LR)-fuzzy analytical hierarchy process(FAHP) method(LR-FAHP). A susceptibility evaluation index system for different kinds of landslides was built up by sorting the importance of all index factors and calculating their weights,and susceptibility level prediction of regional landslides was performed based on GIS platform. The results indicates that,compared with LR,FAHP and FR models,WFR model can promote the evaluation accuracy by 4%‐9%,proving that the LR-FAHP method is a good method for calculating the weight of evaluation index factors. It is also shown that the success rate and the prediction rate of susceptibility evaluation by WFR model with landslide classification reach 79.2% and 79.6% respectively,which are respectively better than those by WFR model without landslide classification. The model can provide a reliable approach for building up evaluation index system and regional landslide susceptibility.
According to the regional landslide features,an evaluation index system developed based on the characteristics among different types of landslides,can make landslide susceptibility evaluation more scientific and accurate. Taking Wanzhou district of Three Gorges Reservoir as an example,the regional landslides were divided into steeply inclined strata landslides and gently inclined strata landslides based on geological situation,landslide spatial distribution and characteristics. Twelve index factors including elevation difference,slope angle,aspect,plane and profile curvatures,stratum lithology,water distribution,geological structure,highway,stratum dip,rainfall and weak interlayer containing montmorillonite were obtained to build the basic evaluation system,and a weighted frequency ratio model(WFR) was proposed to perform regional landslide susceptibility evaluation based on the logistic regression(LR)-fuzzy analytical hierarchy process(FAHP) method(LR-FAHP). A susceptibility evaluation index system for different kinds of landslides was built up by sorting the importance of all index factors and calculating their weights,and susceptibility level prediction of regional landslides was performed based on GIS platform. The results indicates that,compared with LR,FAHP and FR models,WFR model can promote the evaluation accuracy by 4%‐9%,proving that the LR-FAHP method is a good method for calculating the weight of evaluation index factors. It is also shown that the success rate and the prediction rate of susceptibility evaluation by WFR model with landslide classification reach 79.2% and 79.6% respectively,which are respectively better than those by WFR model without landslide classification. The model can provide a reliable approach for building up evaluation index system and regional landslide susceptibility.
引文
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[21]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(增2):3 978-3 985.(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(Supp.2):3 978-3 985.(in Chinese))
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[23]POURGHASEMI H R,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.
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[2]AKGUN A,KINCAL C,PRADHAN B.Application of remote sensing data and GIS for landslide risk assessment as an environmental threat to Izmir city(west Turkey)[J].Environmental Monitoring and Assessment,2011,184(9):5 453-5 470.
[3]张俊,殷坤龙,王佳佳,等.三峡库区万州区滑坡灾害易发性评价研究[J].岩石力学与工程学报,2016,35(2):284-296.(ZHANGJun,YIN Kunlong,WANG Jiajia,et al.Evaluation of landslide susceptibility for Wanzhou District of Three Gorges Reservoir[J].Chinese Journal of Rock Mechanics and Engineering,2016,35(2):284-296.(in Chinese))
[4]王佳佳,殷坤龙,肖莉丽.基于GIS和信息量的滑坡灾害易发性评价[J].岩石力学与工程学报,2014,33(4):797-808.(WANG Jiajia,YIN Kunlong,XIAO Lili.Landslide susceptibility assessment based on gis and weighted information value:a case study of Wanzhou District,Three Gorges Reservoir[J].Chinese Journal of Rock Mechanics and Engineering,2014,33(4):797-808.(in Chinese))
[5]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].Computer and Geosciences,2013,51(2):350-365.
[6]YALCIN A,REIS S,AYDINOGLU A C,et al.A GIS-based comparative study of frequency ratio,analytical hierarchy process,bivariate statistics and logistics regression methods for landslide susceptibility mapping in Trabzon,NE Turkey[J].Catena,2011,85(3):274-287.
[7]冯杭建,周爱国,俞剑君,等.浙江梅雨滑坡易发性评价模型对比[J].地球科学,2016,41(3):403-415.(FENG Hangjian,ZHOUAiguo,YU Jianjun,et al.A Comparative study on plum-rain-triggered landslide susceptibility assessment models in west Zhejiang province[J].Earth Science,2016,41(3):403-415.(in Chinese))
[8]胡瑞林,范林峰,王珊珊,等.滑坡风险评价的理论与方法研究[J].工程地质学报,2013,21(1):76-84.(HU Ruilin,FAN Linfeng,WANG Shanshan,et al.Theory and method for landslide risk assessment-current status and future development[J].Journal of Engineering Geology,2013,21(1):76-84.(in Chinese))
[9]LEE S,RYU J H,LEE M J,et al.Use of an artificial neural network for analysis of the susceptibility to landslides at Boun,Korea[J].Environmental Geology,2003,44(7):820-833.
[10]VASU N N,LEE S R.A hybrid feature selection algorithm integrating an extreme learning machine for landslide susceptibility modeling of Mt.Woomyeon,South Korea[J].Geomorphology,2016,263:50-70.
[11]YESILNACAR E,TOPAL T.Landslide susceptibility mapping:Acomparison of logistic regression and neural networks methods in a medium scale study,Hendek region(Turkey)[J].Engineering Geology,2005,79(3):251-266.
[12]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-SVMmodel[J].Geography and Geo-Information Science,2016,32(3):23-28.(in Chinese))
[13]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))
[14]HUANG F M,YIN K L,HUANG J S,et al.Landslide susceptibility mapping based on self-organizing-map network and extreme learning machine[J].Engineering Geology,2017,223:11-22.
[15]朱云波,余斌,王治兵,等.四川德昌茨达“8·24”群发性滑坡型泥石流之地形条件[J].山地学报,2015,33(1):108-115.(ZHUYunbo,YU Bin,WANG Zhibing,et al.Topographic research of group-occurring landslide-induced debris flow in Dechang,Sichuan[J].Mountain Research,2015,33(1):108-115.(in Chinese))
[16]吴益平,张秋霞,唐辉明,等.基于有效降雨强度的滑坡灾害危险性预警[J].地球科学,2014,39(7):889-895.(WU Yiping,ZHANGQiuxia,TANG Huiming,et al.Landslide hazard warning based on effective rainfall intensity[J].Earth Science,2014,39(7):889-895.(in Chinese))
[17]ZHOU C,YIN K L,CAO Y,et al.Landslide susceptibility modeling applying machine learning methods:A case study from Longju in the Three Gorges Reservoir area,China[J].Computers and Geosciences,2018,112:23-37.
[18]ERENER A,MUTLU A,DüZGüN H S.A comparative study for landslide susceptibility mapping using GIS-based multi-criteria decision analysis(MCDA),logistic regression(LR)and association rule mining(ARM)[J].Engineering Geology,2016,203:45-55.
[19]刘艺梁,殷坤龙,刘斌.逻辑回归和神经网络在滑坡灾害空间预测中的应用[J].水文地质工程地质,2010,37(5):92-96.(LIUYiliang,YIN Kunlong,LIU Bin.Application of logistic regression and artificial neural networks in spatial assessment of landslide hazards[J].Hydrogeology and Engineering Geology,2010,37(5):92-96.(in Chinese))
[20]KAYASTHA P,DHITAL M R,SMEDT F D.Application of the analytical hierarchy process(AHP)for landslide susceptibility mapping:A case study from the Tinau watershed,west Nepal[J].Computers and Geosciences,2013,52:398-408.
[21]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(增2):3 978-3 985.(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(Supp.2):3 978-3 985.(in Chinese))
[22]胡启芳,范雷,董治军,等.基于模糊综合评价的改进层次分析法在滑坡危险性评价中的应用[J].长江科学院院报,2014,31(5):29-33.(HU Qifang,FAN Lei,DONG Zhijun,et al.Application of modified analytic hierarchy process based on fuzzy comprehensive evaluation to landslide risk assessment[J].Journal of Yangtze River Scientific Research Institute,2014,31(5):29-33.(in Chinese))
[23]POURGHASEMI H R,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.
[24]CHOI J,OH H J,LEE H J,et al.Combining landslide susceptibility maps obtained from frequency ratio,logistic regression,and artificial neural network models using ASTER images and GIS[J].Engineering Geology,2012,124(1):12-23.
[25]张俊.三峡库区万州区滑坡灾害风险评估研究[硕士学位论文][D].武汉:中国地质大学,2016.(ZHANG Jun.Landslide risk assessment in Wanzhou Conunty,Three Gorges Reservoir[M.S.Thesis][D].Wuhan:China University of Geosciences,2016.(in Chinese))
[26]桂蕾,殷坤龙,刘长春.基于集对分析的巴东新城区滑坡灾害空间预测[J].岩石力学与工程学报,2012,31(增1):2 653-2 659.(GUI Lei,YIN Kunlong,LIU Changchun.Spatial prediction of landslide hazards in new site of Badong county based on set pair analysis[J].Chinese Journal of Rock Mechanics and Engineering,2012,31(Supp.1):2 653-2 659.(in Chinese))
[27]SCHICKER R,MOON V.Comparison of bivariate and multivariate statistical approaches in landslide susceptibility mapping at a regional scale[J].Geomorphology,2012,161-162(7):40-57.
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