摘要
滑坡灾害易发性评价研究对规划灾害区域、制定防灾策略等方面具有十分重要的意义。以滑坡灾害频发的汶川及周边两县(理县和茂县)为例,提出滑坡灾害易发性评价的快速聚类-信息量模型。选取坡度、高程、坡向、距构造的距离、距水系的距离、地层岩性和土地利用情况为对滑坡有重要影响的7个影响因子,并在二级因子的分类上,对上述前5个影响因子依据159处滑坡样本分别开展快速聚类分析,同时也给出了传统的等距分类法,以便与快速聚类方法形成对比,对后2个影响因子则以定性方法分类。根据上述二级分类方法的不同,以及滑坡样本是否考虑面积因素,将信息量模型细分为四类(模型a:快速聚类-数量模型、模型b:等距分类-数量模型、模型c:快速聚类-面积模型、模型d:等距分类-面积模型),分别计算各二级指标信息量,并通过ArcGIS空间叠加分析得到研究区域信息量分布,然后通过自然断点法将研究区滑坡易发性划分为五个等级。以易发性递增原则和线下面积(Area Under Curve,AUC)作为精度评价指标,结果表明:①快速聚类模型(模型a和模型c)整体效果优于等距分类模型(模型b和模型d);②相同分类方法下,面积模型(模型c与模型d)整体优于数量模型(模型a和模型b);③在上述两项优势的加持下,模型c相较于模型b,评价精度明显提升,其AUC值从80.46%提高到87.25%。
The study of landslide susceptibility evaluation is of great significance to both zoning of geological disasters and disaster prevention strategies.Taking Wenchuan and two surrounding counties(Li County and Mao County),which are prone to landslides,as an example,K-means cluster information model for landslide susceptibility mapping is proposed.After seven impact factors,i.e.,slope angle,elevation,aspect,distance from the structure,distance from the water system,formation lithology and the land usage,are selected,the secondary classification for factors is carried out.The former five impact factors mentioned above were classified separately by K-means cluster analysis according to 159 landslide samples.At the sametime,the traditional isometric classification was also presented to compare with the K-means clustering method.The latter two impact factors were classified qualitatively.According to the differences of the above secondary classification methods and whether the landslide sample considers the area factor,the information model is subdivided into four categories(model a:K-means clustering quantitative model,model b:isometric classification quantitative model,model c:K-means clustering area model,and model d:isometric classification area model).The information of each secondary index was calculated separately,and the information distribution of the study area was obtained through spatial overlay analysis of ArcGIS.Then,the landslide susceptibility of the study area was divided into five grades by natural breakpoint method.Taking the principle of increasing susceptibility and Area Under Curve(AUC)as the accuracy evaluation indicators,three results were obtained.First,the overall effect of K-means clustering models(model a and model c)is better than that of isometric classification models(model b and model d).Second,the area models(model c and model d)are generally better than the quantitative models(model a and model b)under the same classification method.Third,With the above two advantages,the evaluation accuracy of model c is significantly improved compared with model b,and its AUC value is increased from 80.46%to 87.25%.
作者
周天伦
曾超
范晨
毕鸿基
龚恩慧
刘晓
ZHOU Tianlun;ZENG Chao;FAN Chen;BI Hongji;GONG Enhui;LIU Xiao(Three Gorges Research Center for Geo-hazard of Education,China University of Geosciences,Wuhan,Hubei 430074,China;CCCC Second Highway Consultants Co.Ltd.,Wuhan,Hubei 430056,China)
出处
《中国地质灾害与防治学报》
CSCD
2021年第5期137-150,共14页
The Chinese Journal of Geological Hazard and Control
基金
国家自然科学基金面上项目(42072314,41572279)
国家重点研发计划项目(2017YFC1501304)
中国博士后科学基金项目(2014T70758,2012M521500)
中交第二公路勘察设计研究院有限公司科技研发项目(WFYZ-2018-049-001)。
关键词
滑坡易发性评价
快速聚类
信息量模型
GIS
landslide susceptibility assessment
K-means cluster
information model
GIS