GIS支持下基于CF方法的2013年芦山地震滑坡因子敏感性分析

本文依据研究区现有地形因子(高程、坡度、坡向、斜坡曲率)、地质因子(岩性、距深大活动断裂距离)和地震因子(PGA、距震中距离)等相关影响因子资料并结合地震区基本情况,以GIS技术作为操作平台,采用确定性系数法,开展较为详细的芦山地震区地震滑坡影响因子敏感性分析工作。首先,以GIS技术作为操作平台将地震滑坡的8个影响因子据研究区特征进行分级,构建不同影响因子分级的栅格图层进行地震滑坡分布相关参数统计; 其次,采用确定性系数法计算8个地震滑坡影响因子分级区间对应的CF值,分别提取出地震滑坡最为敏感分级区间以进行地震滑坡影响因子敏感性分析,从而衡量不同影响因子分级区间对地震滑坡易发性敏感程度。地震滑坡影响因子敏感性分析结果表明,除斜坡曲率因子与距震中距离因子对地震滑坡易发性不敏感外,其他6个影响因子对地震滑坡易发性均很敏感,分别是影响滑坡发生的主要地形、地质和地震因子。针对斜坡曲率因子和距震中距离因子对滑坡的易发性不是很敏感是否受内部其他影响因子限制所进行的分析与讨论结果表明,SW向很可能限制了斜坡曲率对地震滑坡易发性的敏感程度,地层岩性对距震中距离因子限制作用更为明显,除奥陶系、志留系外的地层岩性对距震中距离因子敏感性程度都有限制。文章所得成果具有一定的方法理论意义,对于防震减灾工作也具有一定参考意义。     

基于范数灰色理论的滑坡稳定性敏感因子分析

灰色关联分析模型可用于滑坡稳定性敏感因子的定量分析。将范数灰色理论引入到滑坡稳定性敏感因子分析之中,确定了影响滑坡稳定性系数的因素权重,较为直观地反映了各影响因子对滑坡稳定性的影响程度。以重庆市綦江县土台镇滑坡为例,进行滑坡稳定性敏感因子的范数灰关联度计算。结果显示,滑动面岩土体强度参数是影响土台镇滑坡稳定性最重要的因素,地震作用和地下水位的变化对该滑体稳定性的影响要稍弱一些。     

滑坡敏感因子的灰色关联分析

提出了用灰色关联模型对滑坡进行敏感性分析,建立了以滑坡敏感性影响因子为子序列、以其稳定性系数为母序列、以极差变化为数据转化方法、以关联度为评价结果的滑坡敏感因子分析灰色关联模型。本文以三峡库区水田坝下土地岭滑坡工程为例,选取滑带凝聚力c, 内摩擦角φ, 地震加速度及库水位为子序列,以剩余推力法计算的滑坡稳定性系数为母序列,得出了滑带的c,φ值对滑坡稳定性影响最为敏感的结论。     

云南小江流域滑坡关键影响因子研究

确定诱发滑坡失稳的关键因素是滑坡研究的一个重要内容。采用不同影响因子图层进行危险性分区结果存在明显差异,这是由于第一因子对于滑坡变形失稳的贡献程度不同,即不同影响因子与滑坡的相关性不同。在进行滑坡灾害分析时,必须首先确定影响滑坡的关键因子以建立准确的统计分析模型。采用滑坡确定性系数的合并检验方法,在GIS中对云南小江流域进行了滑坡影响因子分析,并确定了影响滑坡的关键性因子。据此建立的多元统计分析预测模型经检验具有较高精度,要以为小江流域的灾害防治、规划建设提供科学依据。     

基于ArcGIS模型的滑坡地质灾害敏感性评价——以金沙江上游为例

针对一些灾害评价模型过于复杂,影响因子选择过多的情况,在前人研究的基础上,选取坡度、工程地层岩性、断裂构造、地形起伏度4个因素作为滑坡地质灾害的影响因子。以金沙江上游白玉至巴塘段为研究区域,利用GIS技术,基于信息量模型,分析不同因子对滑坡地质灾害的敏感性,并利用信息量值绘制出滑坡灾害敏感图。结果表明,滑坡灾害点分布与敏感性分级具有显著相关性。评价方法可对类似金沙江上游这样的山地区域性滑坡灾害敏感性评价提供思路和参考。     

云南省新平县滑坡预警区划研究

以新平县为研究区,采用确定性系数(CF)方法分析滑坡与地质环境因子各区段(类型)之间的敏感性关系,得出各因子不同区段(类型)的敏感性大小,并运用Logistic回归分析方法建立预警区划模型,认为坡度、岩组、年均降雨量、高程、构造等五个因子是影响研究区滑坡发生的敏感性因子,建立Logistic概率预测模型,并对研究区进行五级预警区划,区划结果对该区预警工作有积极的指导意义。     

基于影响因素分布模型的滑坡稳定性敏感分析

提出了基于影响因素分布模型的滑坡稳定性敏感分析方法,该法用于分析影响因素在整个定义域内对滑坡稳定性的敏感程度,以滑坡影响因素为子序列,以其稳定性系数为目标序列,根据子序列的累积分布函数求影响因素区间概率函数,与目标序列差值乘积加和得到敏感性综合决策值。以三峡库区奉节县太山庙滑坡为例,经类比统计得到滑带土抗剪强度参数的分布函数,通过经验分析得到地震水平加速度和库水位变化的分布函数,应用该法计算得到影响因素中对滑坡稳定性最敏感的因素。以内摩擦角为例,分析了影响因素方差与滑坡稳定性敏感分析结果的关系。     

基于GIS的天水市滑坡危险性评价

本文通过统计学方法提取可能影响该地区滑坡的因子。并依据二元逻辑回归结果．利用GIS空间分析和建模功能,对研究区滑坡进行建模,再由相关性等级分析方法进一步获取相关影响因子对滑坡的影响范围,最终得出该区域滑坡危险性评价图。而在不同分辨率尺度上,对滑坡产生影响的因子有所相同,当分辨率由1000m-60m时,影响因子明显增多。以逻辑回归模型自身精度、滑坡实际发生比和坡度法为模型判定标准,发现120m分辨率下的滑坡发生概率模型能有效表达研究区滑坡发生状况。     

滑坡灾害坡度坡向敏感性分析研究——以云南大关县为例

借助地理信息系统(GIS)空间分析技术和基于贡献率的敏感性分析方法,分析地形地貌因子与滑坡敏感性之间的关系,研究大关县坡度坡向对滑坡的影响程度的大小。将坡度和坡向因子细分区间,计算每一区间对滑坡的贡献率,定量地分析坡度、坡向区间变化与滑坡发育的关系。研究结果表明,坡度15°~25°区间的区域为滑坡最敏感的区域,坡向带270°~315°区间是滑坡灾害最敏感的区域,但坡向带每一区域的贡献率差别不大,说明坡向对滑坡发生的作用效果不显著。     

基于CF概率模型的滑坡致滑地质环境背景因子筛选分析

基于福建省地质灾害调查成果数据库,对福建省内滑坡的致滑地质环境背景因子分别进行描述统计分析和CF概率模型计算,筛选出滑坡致滑关键地质环境背景因子,按敏感程度从高到低分别为滑坡所在位置高程、斜坡坡度、土层厚度和基岩岩性。     

基于GIS的分组数据Logistic模型在斜坡稳定性评价中的应用

分组数据Logistic回归是针对因变量为定性变量、自变量为分类变量的一种解决方案,加权最小二乘法可用来求解该方程.将巫山县新城西区作为试验区,选取岩性、坡度、高程、地下水位埋深、距最近有影响构造线距离5种因素为斜坡稳定性影响因素,以试验区历史滑坡发生为因变量,建立了区域斜坡稳定性评价的分组数据Logistic回归方程,进行了回归方程显著性检验和回归系数显著性检验,最后利用回归方程对全区斜坡稳定性进行预测.模型拟合精度为:以滑坡发生概率0.157 9为判据,滑坡发生样本的判对率为72.55%,滑坡不发生样本的判对率为79.69%.     

Applying weight of evidence method and sensitivity analysis to produce a landslide susceptibility map

The main purpose of this study is to define the main variables that contribute to the occurrence of landslides in Kimi, Euboea, Greece, and to produce a landslide susceptibility map using the weight of evidence method. For the developed model, a sensitivity analysis is carried out in order to understand the model’s behavior when small changes are introduced in the weight value of the landslide-related variables. Landslide locations were identified from field surveys and interpretation of aerial photographs which resulted in the construction of an inventory map with 132 landslide events, while eight contributing variables were identified and exploited. All landslide-related variables were converted into a 5?×?5-m float-type raster file. These input-raster layers included a lithological unit layer, an elevation layer, a slope angle layer, a slope aspect layer, a distance from tectonic features layer, a distance from hydrographic network layer, a topographic wetness index layer, and a curvature layer. The validation of the developed model was achieved by using a subset of unprocessed landslide data, showing a satisfactory agreement between the expected and existing landslide susceptibility level, with the area under the predictive rate curve estimated to be 0.808. The area under the success rate curve was estimated to be 0.828 indicating a very high classification rate for existing landslide areas. According to the results of the sensitivity analysis, the lithological unit “yellowish gray to white marls” was the most sensitive as it had the highest change in the relative frequency of observed landslides. The overall outcomes of the performed analysis provide crucial knowledge in successful land use planning and management practice and also in risk reduction projects.     

基于GIS和层次分析法的沙溪流域滑坡地质灾害易发性评价

滑坡是沙溪流域主要地质灾害类型之一,开展滑坡灾害易发性评价可为区域地质灾害防治提供数据基础和决策依据。通过沙溪流域生态地质调查,分析了滑坡灾害分布规律和影响因素之间的关系,选取岩性建造、地貌、坡度、坡向、降雨量、距河流距离和距断层距离7项指标,利用层次分析法及地理信息系统空间分析技术,开展沙溪流域滑坡地质灾害易发性评价。结果显示: 沙溪流域滑坡易发性影响因子依次为岩性建造、多年年均降水量、地形地貌、坡度、距河流距离、距断层距离和坡向; 沙溪流域滑坡灾害易发性与坡度、岩性建造、年均降水量表现出明显正相关,即坡度越大、岩性建造性质越软弱、越易风化,年均降水量越多,越易引发滑坡灾害; 滑坡灾害易发性与断裂构造、河流距离与滑坡灾害易发性呈负相关,即距离越近越容易诱发地质灾害; 流域整体以低易发区和极低易发区为主,高易发区主要分布在沙溪流域中南部、东部及东北部地区。这为沙溪流域地质灾害防治提供了基础数据和决策依据。     

汶川地震触发崩滑地质灾害空间分布及影响因素

通过遥感解译和实地考察,获取了2008年汶川地震触发崩滑的空间分布,利用GIS空间分析和Lo-gistic回归,分析崩滑的空间分布特征及其影响因素,建立了地震触发崩滑与其影响因素之间的回归方程。结果表明,(1)研究区共有5 154个崩滑群,覆盖总面积1 139 km2;(2)崩滑沿北川—映秀发震断层的两侧(断层上盘区占90%),呈北东向宽度不一的条带状分布;(3)Ⅺ和Ⅹ烈度区崩滑面积占区域面积的73.2%,Ⅷ度及以下烈度区崩滑面积比例较小;(4)崩滑发育及空间分布不仅受控于发震断层的活动,断层上下盘效应、地形放大效应等也是其重要影响因素。崩滑与其影响因子的回归方程表明:(1)到北川—映秀发震断层距离因子和到震中距离因子的偏回归系数远大于其他因子的偏回归系数,北川—映秀断层发震活动是控制崩滑空间分布的主导因子;(2)岩性软硬程度对崩滑空间分布的影响不显著;(3)地形坡度、高程、坡度变率、多年累积降雨、人工修路及植被覆盖对崩滑的发育产生影响。地形高程因子对崩滑空间分布的影响大于坡度、坡度变率因子的影响。人工道路、多年降雨及植被覆盖对地震崩滑的影响程度依次降低。     

基于集成学习与径向基神经网络耦合模型的三峡库区滑坡易发性评价

准确的滑坡易发性评价结果是滑坡风险评价的重要基础.为提升滑坡易发性评价精度,以三峡库区龙驹坝为例,选取坡度等10个因子构建滑坡易发性评价指标体系,应用频率比方法定量分析各指标与滑坡发育的关系.在此基础上,随机选取70%/30%的滑坡数据作为训练/测试样本,应用径向基神经网络和Adaboost集成学习耦合模型(RBNN-Adaboost),径向基神经网络和逻辑回归模型分别开展易发性评价.结果显示:水系距离、坡度等是滑坡发育的主控因素;RBNN-Adaboost耦合模型的预测精度最高(0.820),优于RBNN模型和LR模型的0.781和0.748.Adaboost集成算法能进一步提升模型的预测性能,所提出的耦合模型结合了两者的优点,具有更强的预测能力,是一种可靠的滑坡易发性评价模型.      

Application of logistic regression for landslide susceptibility zoning of Cekmece Area, Istanbul, Turkey

As a result of industrialization, throughout the world, cities have been growing rapidly for the last century. One typical example of these growing cities is Istanbul, the population of which is over 10 million. Due to rapid urbanization, new areas suitable for settlement and engineering structures are necessary. The Cekmece area located west of the Istanbul metropolitan area is studied, because the landslide activity is extensive in this area. The purpose of this study is to develop a model that can be used to characterize landslide susceptibility in map form using logistic regression analysis of an extensive landslide database. A database of landslide activity was constructed using both aerial-photography and field studies. About 19.2% of the selected study area is covered by deep-seated landslides. The landslides that occur in the area are primarily located in sandstones with interbedded permeable and impermeable layers such as claystone, siltstone and mudstone. About 31.95% of the total landslide area is located at this unit. To apply logistic regression analyses, a data matrix including 37 variables was constructed. The variables used in the forwards stepwise analyses are different measures of slope, aspect, elevation, stream power index (SPI), plan curvature, profile curvature, geology, geomorphology and relative permeability of lithological units. A total of 25 variables were identified as exerting strong influence on landslide occurrence, and included by the logistic regression equation. Wald statistics values indicate that lithology, SPI and slope are more important than the other parameters in the equation. Beta coefficients of the 25 variables included the logistic regression equation provide a model for landslide susceptibility in the Cekmece area. This model is used to generate a landslide susceptibility map that correctly classified 83.8% of the landslide-prone areas.     

Landslide susceptibility mapping of Tevankarai Ar sub-watershed,Kodaikkanal taluk,India, using weighted similar choice fuzzy model

This paper deals with the landslide susceptibility zonation of Tevankarai Ar sub-watershed using weighted similar choice fuzzy method in a GIS environment. There has been a rapid increase in landslide occurrences in the Kodaikkanal town and area surrounding the town specially in the settlements around the town and road links leading to and from the town. This necessitates a detailed study of slope instability problems in this area. It is observed that these incidences occur frequently during the monsoon and summer showers. Rainfall is identified as the prime triggering factor. Eleven physical factors that cause instability are identified as causative factors from the field investigations and landslide occurrences. Land use pattern, slope gradient, curvature and aspect, weathering index which are evaluated from the weathering ratios of different chemical constituents of the three major lithological variations, soil type, hydraulic conductivity of soil and soil thickness, geomorphology, drainage, and lineament have been utilized to prepare the spatial variation. A weighted similar choice fuzzy model which ranks a set of alternatives by identifying the similarity between the outcome of alternatives and outcome of ideal alternatives is used to rank the causative factors. Each causative factor is classified into sub-categories and rated based on their effect on stimulating the landslide event using qualitative judgment derived from field studies and landslide history. The prepared thematic maps of causative factors are integrated, utilizing the GIS software Arcmap. The outcome has projected the low, moderate, high, and very high landslide susceptibility zones. The high-hazard and very high-hazard areas fall in the northwestern part characterized by croplands and agricultural plantations, while the moderate hazard zones are seen in prominent settlements and low-hazard zones are observed in the sparse settlements and zones of less agricultural activity. The model is verified using the relative landslide density (R) index, and the susceptibility map is found to be consistent with the mapped landslide incidences. The results from this study illustrate that the use of weighted similar choice fuzzy method is suitable for landslide susceptibility mapping on regional scale in growing hill towns as Kodaikkanal town.     

湖南雪峰山地区降雨型滑坡灾害敏感性区划

雪峰山地区是湖南省降雨型滑坡灾害较为发育的地区之一,滑坡灾害给该地区社会及经济发展造成严重影响,给人民生命财产造成严重损失.本文选择影响滑坡发生的关键地质因子,即坡向、高程、工程岩组、斜坡类型和坡度等5个因子,通过确定性系数（CF）与层次分析法（AHP）的融合,解决评价中各因子指标的排序赋值和各评价因子叠加的权重问题.在给定因子权重的基础上,采用因子权值与因子赋值相乘后相加的方法,求得雪峰山地区降雨型滑坡综合敏感度值,并根据敏感性指标对研究区进行敏感性区划.     

Identification of landslide susceptibility zones in Gish River basin,West Bengal,India

The occurrence of landslide in the hilly region of Darjeeling during monsoon season is a matter of serious concern. Every year this natural hazard damages the major roads at several places and thus disrupts the transport and communication system in this region. This paper tries to prepare a landslide susceptibility zone (LSZ) map for the Gish River basin. A total number of 16 spatial parameters have been taken for this study and these are categorised under six factor clusters or groups for example, triggering factors, protective factor, lithological factors, morphometric factors, hydrological factors and anthropogenic factors. The LSZ map is prepared by integrating all the parameters adopting the weighting base as logistic regression. The landslide susceptibility map shows that nearly 9.11% of the area falls under the very high landslide-susceptible zone while 40.28% of the area of the total basin lies under the very low landslide-susceptible zone. The landslide-susceptible model is validated through the receiver operating characteristic curve. This curve shows 86% success rate in defining landslide-susceptible zones and 83.40% prediction rate for the occurrence of landslides. The spatial relationship between the landslide susceptibility model and other factors’ groups shows that the morphometric factors’ cluster (mainly slope) is the focalone for the determination of landslide-susceptible zone.