滑坡风险评价的理论与方法研究

作为防灾减灾的重要措施之一,滑坡风险评价已经成为近年来国际上滑坡研究的热点,并形成了较为完备的滑坡风险管理体系。国内的滑坡风险研究则起步较晚,滑坡风险评价的关键支撑技术体系尚未建立。本文对滑坡风险评价中的关键理论和方法进行梳理,阐述了国际滑坡风险评价的理论框架和技术流程,介绍了国内外滑坡易发性、危险性和风险评价的最新进展,评述了滑坡易发性评价、扩展范围预测、频率分析以及承灾体易损性评价的主要方法,阐明了现阶段滑坡风险评价的重点领域和前沿科学问题,并对滑坡灾害的风险评价提出了三点展望。     

地质灾害风险调查的方法与实践

风险管理是一门新兴的管理学科,风险调查是风险源识别、分析、评价和风险处置的基础。在分析国内外地质灾害风险管理进展和差异的基础上,提出了中国地质灾害风险管理中术语统一的意见,论述了地质灾害风险调查的类型和精度,风险的分级,不同精度下风险调查的内容和方法,以及风险调查的技术要点。并以陕西省延安市市区和虎头峁场址风险调查为例,分别阐述了1∶10000、1∶1000比例尺精度下的风险调查和区划的过程、结果。     

陕西宝鸡地区胡家山滑坡风险性评价

在综合分析国内外滑坡风险评价方法的基础上,通过对野外地质灾害的调查、对胡家山滑坡的勘查和岩土物理力学参数的测试分析,利用Geo-slope软件和经验方法对滑坡多级潜在滑面进行稳定性分析、影响范围预测和失稳概率分析,开展了对滑坡在天然状态、10年一遇降雨和50年一遇降雨3种工况条件下的危险性、承灾体易损性分析等,完成了胡家山滑坡风险性评价,探讨了单体滑坡风险评价的技术方法与流程。根据评价结果,胡家山滑坡财产最大风险为113.71万元/年,人口最大风险为0.0648人/年,其人口风险超过了社会可容许的风险标准。因此,应采取加强监测、搬迁避让、适当工程治理等方法进行风险控制,以达到防灾减灾的目的。     

滑坡灾害风险评价的关键理论与技术方法

滑坡灾害风险评估主要包括滑坡敏感性分析、危险性评价和风险评估3个不同层次的内容。但是,滑坡地质灾害本身的复杂性和滑坡强度的确定、滑坡发生的时空概率估算、承灾体的易损性时空概率分析等难点问题的存在,无疑阻碍了滑坡风险定量评估的推广和应用。在系统分析国内外滑坡灾害风险评估研究成果的基础上,对滑坡灾害风险评价的技术体系进行了总结,提出了不同层次滑坡灾害的研究内容和相应的评价方法;分析了实现滑坡风险有效评价涉及到的难点问题,并结合降雨和地震诱发的滑坡灾害危险性评价国内外的实践,提出了中国未来滑坡灾害风险评价研究的主要内容和技术方法。     

陕北黄土高原不同地貌类型区黄土滑坡频率分布

黄土滑坡是西北地区最为严重的地质灾害.频率分布对于区域滑坡风险评估具有重要的意义,借鉴粒度分析方法研究黄土滑坡分布情况,提出滑坡规模径概念,并通过Gamma分布函数对滑坡规模频率曲线进行了拟合.结果表明:(1) 在区域尺度上,可以借鉴粒度分析的理论和方法分析区域滑坡规模百分含量;(2) 在双对数坐标下,频率曲线具有“偏转效应”,而Gamma分布函数在描述滑坡规模径频率分布方面具有广泛的适应性,能够很好地拟合黄土滑坡规模频率;(3) 无论是黄土滑坡的数量还是规模,以墚为主的黄土丘陵区宝塔区都是受滑坡灾害威胁最为严重的区域;但黄土地貌在由塬向峁区的演变过程中,黄土滑坡规模变异或离散程度逐渐减小.      

降雨型滑坡定量风险评价研究——以宣恩县干坝滑坡为例

单体滑坡定量风险评价一直是滑坡研究领域的重点及难点,特别是滑坡危险程度和易损性的确定。本文以湖北宣恩县干坝滑坡为例,进行降雨型滑坡定量风险评价研究;在降雨极值分析的基础上,计算不同降雨重现期下滑坡渗流场和稳定性变化规律;在滑坡影响范围分析的基础上,采用定性方法确定不同区域承灾体的易损性,定量分析不同降雨重现期下逐个建筑物和室内人员风险,通过建立人口和财产总风险与年超越概率关系曲线计算滑坡年风险;通过三维数字模型展示建筑物和室内人员在不同降雨工况下的风险,利于主管部门对单体滑坡的风险管控。     

滑坡风险评价难点及方法综述

滑坡风险评价是从国外引进的新理念和新方法,国内目前对此的理解和应用,甚至概念都有混淆之处;同时又由于"风险"的本质是一种未来事件的不确定性,对其评价也有诸多困难。本文根据当今国际上通用的滑坡风险管理理论,得出风险评价要素,进而分析出进行滑坡风险评价的难点,从滑坡的空间预测、时间预测、滑移距离预测和强度预测四个方面,综述了国内外在这些难点上进行量化和评价的技术方法,并对各种方法的优缺点和适用性进行了评述。     

陕西陇县李家下滑坡风险评价

在野外勘查及室内实验的基础上,应用滑坡风险评价方法,对李家下滑坡进行了较为完整的风险评价,包括滑坡失稳概率计算、承灾体承灾概率计算、承灾体易损性计算和承灾体价值核算,最后得出滑坡总风险值,并将风险值用价值的形式量化表示。     

四川虹口黑泥湾滑坡风险性评估

黑泥湾滑坡是降雨诱发的一个土-岩接触面大型滑坡。基于野外详细调查基础上,探讨了黑泥湾滑坡的成灾模式,并利用水平投影法对滑坡多级潜在滑面进行了稳定性分析;在10年一遇降雨、50年一遇降雨及100年一遇降雨3种工况下,对黑泥湾滑坡开展了风险性评估、风险区划及社会风险评价。结果表明,黑泥湾滑坡财产最大风险为111.666万元/a,人口最大风险为0.002 23人/a;同时区划结果显示1、2、3号受险区的单人生命风险最大,具有大于10-3的生命风险,其人口社会风险值已经处于不可接受风险区,应采取加强监测、搬迁避让、适当工程治理等方法进行风险控制,以达到防灾减灾的目的。     

滑坡易发性评价方法和精度比较——以贵州省大方县为例

近年来贵州省突发性滑坡地质灾害时有发生,除在册滑坡隐患外,还有不少斜坡存在着滑坡的孕灾环境条件,通过新一轮的地质灾害风险评价发现,选用不同的风险评价体系对地质灾害易发性的影响很大,从而影响地质灾害防治、国土空间规划和政府决策等基础数据。本次以大方县滑坡数据为例,选取与滑坡相关的7个影响因子:坡度、坡向、相对高差、工程地质岩组、距水系距离、距构造距离以及土地利用类型,采用层次分析法(AHP)、信息量模型(I)及耦合模型(AHP-I)对研究区进行滑坡易发性评价,并采用滑坡点频率统计和成功率曲线(ROC)对3种模型的评价精度进行检验。通过比较,选取精度高的耦合模型(AHP-I)作为滑坡易发性评价方法,从而能更加精确地评价大方县的滑坡易发性,为山区县级区域滑坡灾害的防灾减灾提供决策依据与参考。     

基于不同位移预测模型的地震滑坡危险性评估研究-以天水地区为例

近年来,Newmark累积位移分析方法经过不断的改进和应用成为国际主流的地震滑坡危险性评估方法之一,众多学者基于位移预测模型开展区域地震滑坡危险性评估,然而鲜有针对不同位移模型对评估结果影响的定量研究。以天水地区为例,基于不同的位移预测模型开展地震滑坡危险性评估,对比位移模型对地震滑坡危险性评估的影响,探讨建立适用于我国的Newmark位移预测模型。结果表明:基于不同位移预测模型评估所得的地震滑坡危险性结果整体趋势一致,均能区分区域地震滑坡危险性等级的相对差异,但在同样的危险性分级标准下,所得中、高危险区的分布范围有较大差异。这与位移模型的函数形式及其区域相关性有关,在引入Newmark累积位移分析方法开展地震滑坡危险性评估的同时,应尽快建立考虑地震动衰减特征和工程地质背景的Newmark位移预测模型,为中国潜在地震滑坡危险性预测评估、震后滑坡快速评估等提供技术支撑。      

陕西省宝鸡市陈仓区吴家沟滑坡风险评价

吴家沟滑坡是2008年汶川Ms 8.0级大地震触发的黄土塬边的一个中型滑坡。在野外调查、室内分析测试和滑坡稳定性计算的基础上,参照前人的研究成果,分2种情况对该滑坡进行了地质灾害风险评估,为防灾减灾提供决策依据。评估结果表明,在只考虑强降雨对滑坡影响的情况下,吴家沟滑坡个人风险介于7×10-4~4.375×10-5之间,位于严格详细审查区;在考虑强降雨+地震对滑坡影响的情况下,吴家沟滑坡个人风险介于1.32×10-3~8.325×10-5之间,位于不可接受区和严格详细审查区,人员社会风险位于不可接受区。     

A comparison of the predicted vulnerability zones with the data based on hazard zones of landslide in the Kurseong hill subdivision,Darjeeling district,West Bengal,India

Kurseong hill subdivision, being one of the three (Kurseong, Sadar and Kalingpong) subdivisions of the hilly portions of the Darjeeling district, West Bengal, India, is affected by severe landslide incidence in the rainy season every year. These landslides and related phenomena frequently create social and economic instability disrupting communication system, claiming property and even sometimes life. Curbing landslide threat, therefore, becomes very much essential over this area. Individual landslide treatments are seen to be taken up by the construction engineers and geo-technical experts almost every year from government level. But reoccurrence of landslides on the same spots or surrounding places clearly reveals that construction works and filling procedures (usually taken up) are not the adequate measures to heal up the problem unless the area is treated as zones of landslides than individual spots of landslide occurrences. Therefore, the assessment of spatial probability of landslide occurrence in various magnitudes in the form of landslide vulnerability zones becomes essential. This spatial probability should also be compared with temporal probability based on the data of landslide incidence of the area for justification of match or mismatch between the inference drawn from the diagnostic criteria based assessment of the possibility level of landslide occurrence and the reality of the landslide scenario in the light of historical perspective of the area. This comparison will finally help to achieve the predicted vulnerability zones of landslide with desirable accuracy to put forward for planning decision. Moreover, such predicted vulnerability zonation can be taken as a standard estimate to use in planning purpose for the areas where historical data of landslide incidences are inadequate or unavailable. With this view in mind, the present paper takes an attempt to verify and compare landslide vulnerability zones derived from Spatial Terrain Parameter Evaluation (STPE) and Anthropogenic Criteria Identification (ACI) methods with the landslide hazard zones prepared from historical data, i.e. landslide inventory of certain length of time. Careful observation reveals that different degrees of landslide vulnerability zones significantly correspond with the similar magnitudes of the landslide hazard zones determined by past occurrence data of landslides over this hill subdivision and therefore validate the predictability procedure of landslide vulnerability zonation. The accuracy performance of the landslide vulnerability zonation model has further been verified by the occurrence dataset of landslide events through receiver operating characteristic curve analysis where area under curve evaluation showed 81.77 % correctness.     

Quantitative risk assessment of landslide by limit analysis and random fields

Risk may be estimated by multiplying the probability of failure by the consequence. This is acceptable for systems that have a single mode of failure. For systems that have multiple failure modes, such as landslides, the consequences should be assessed individually for each of the failure modes. This paper proposes a new framework of quantitative landslide risk assessment, in which consequences are assessed individually. The framework is generally applicable, and the landslide risk assessments of two typical slopes are presented.     

Landslide susceptibility mapping at Vaz Watershed (Iran) using an artificial neural network model: a comparison between multilayer perceptron (MLP) and radial basic function (RBF) algorithms

Landslide susceptibility and hazard assessments are the most important steps in landslide risk mapping. The main objective of this study was to investigate and compare the results of two artificial neural network (ANN) algorithms, i.e., multilayer perceptron (MLP) and radial basic function (RBF) for spatial prediction of landslide susceptibility in Vaz Watershed, Iran. At first, landslide locations were identified by aerial photographs and field surveys, and a total of 136 landside locations were constructed from various sources. Then the landslide inventory map was randomly split into a training dataset 70 % (95 landslide locations) for training the ANN model and the remaining 30 % (41 landslides locations) was used for validation purpose. Nine landslide conditioning factors such as slope, slope aspect, altitude, land use, lithology, distance from rivers, distance from roads, distance from faults, and rainfall were constructed in geographical information system. In this study, both MLP and RBF algorithms were used in artificial neural network model. The results showed that MLP with Broyden–Fletcher–Goldfarb–Shanno learning algorithm is more efficient than RBF in landslide susceptibility mapping for the study area. Finally the landslide susceptibility maps were validated using the validation data (i.e., 30 % landslide location data that was not used during the model construction) using area under the curve (AUC) method. The success rate curve showed that the area under the curve for RBF and MLP was 0.9085 (90.85 %) and 0.9193 (91.93 %) accuracy, respectively. Similarly, the validation result showed that the area under the curve for MLP and RBF models were 0.881 (88.1 %) and 0.8724 (87.24 %), respectively. The results of this study showed that landslide susceptibility mapping in the Vaz Watershed of Iran using the ANN approach is viable and can be used for land use planning.     

Landslide susceptibility zonation method based on C5.0 decision tree and K-means cluster algorithms to improve the efficiency of risk management

Machine learning algorithms are an important measure with which to perform landslide susceptibility assessments,but most studies use GIS-based classification methods to conduct susceptibility zonation.This study presents a machine learning approach based on the C5.0 decision tree(DT)model and the K-means cluster algorithm to produce a regional landslide susceptibility map.Yanchang County,a typical landslide-prone area located in northwestern China,was taken as the area of interest to introduce the proposed application procedure.A landslide inventory containing 82 landslides was prepared and subse-quently randomly partitioned into two subsets:training data(70％landslide pixels)and validation data(30％landslide pixels).Fourteen landslide influencing factors were considered in the input dataset and were used to calculate the landslide occurrence probability based on the C5.0 decision tree model.Susceptibility zonation was implemented according to the cut-off values calculated by the K-means clus-ter algorithm.The validation results of the model performance analysis showed that the AUC(area under the receiver operating characteristic(ROC)curve)of the proposed model was the highest,reaching 0.88,compared with traditional models(support vector machine(SVM)=0.85,Bayesian network(BN)=0.81,frequency ratio(FR)=0.75,weight of evidence(WOE)=0.76).The landslide frequency ratio and fre-quency density of the high susceptibility zones were 6.76/km2 and 0.88/km2,respectively,which were much higher than those of the low susceptibility zones.The top 20％interval of landslide occurrence probability contained 89％of the historical landslides but only accounted for 10.3％of the total area.Our results indicate that the distribution of high susceptibility zones was more focused without contain-ing more"stable"pixels.Therefore,the obtained susceptibility map is suitable for application to landslide risk management practices.