基于逻辑回归法的滑坡危险度区划研究

滑坡危险度区划能降低由于滑坡所造成的危害,越来越受到重视.结合长江三峡库区秭归-巴东一带滑坡灾害发生的环境和基本特征,选取地层岩性、断裂构造、坡度、坡向、距离河流远近、沟壑密度、高程、植被条件等作为研究区滑坡灾害危险度分区评价因子,利用GIS的空间分析功能将各因素图件栅格化为400m×400m的单元共7461个.采用逻辑回归方法对研究区滑坡灾害进行了危险度分区定量评价,评价结果与该区现有滑坡的分布情况比较吻合.     

基于GIS与AHP耦合技术的土地沙漠化模型研究

晋陕蒙 (西 )地区的土地沙漠化受控于多种因子 ,在详尽分析了土地沙漠化的各种因子基础上 ,确定了母质类型、植被覆盖、地貌类型、沟壑密度、土地利用类型、矿山开发面积、大风强度七个致灾因子 ,并分别建立了它们各自的子专题层图。利用先进的层次分析法 (AHP)确定了影响沙漠化的各致灾因子的权重系数 ,通过地理信息系统 (GIS)与AHP的耦合技术 ,对各子专题层图进行了加权复合叠加 ,利用频率和频数分布直方图 ,确定出土地沙漠化的分区阈值 ,构建了土地沙漠化危险度评价的多源地学信息复合叠加模型 ,并对土地沙漠化危险度进行了分区评价 ,共划分为五个区 ,即强度区、中度区、轻度区、潜在区、未沙区。土地沙漠化评价模型的建立 ,为土地沙漠化的分区评价与预测提供了理论依据 ,使评价结果更科学、合理、准确。     

地理信息系统/遥感技术支持下三峡库区青干河流域滑坡危险性评价

以统计模型为基础、地理信息系统作为工具的滑坡灾害评价模式已经得到普遍认可和使用,数字高程模型(DEM)、遥感影像、区域地质调查资料已经成为区域滑坡评价研究的因子数据源。选择三峡库区青干河流域顺向坡滑坡多发地段为研究区,在滑坡编目数据库基础上,通过:(1)数字高程模型获取高程、坡度、地形聚水能力因子;(2)遥感影像获取植被指数;(3)区域地质调查资料、数字高程模型计算斜坡类型定量因子TOBIA指数及获取岩石地层单元因子。采用二分类变量逻辑回归评价方法对上述6种因子建立滑坡危险性评价模型,开展地理信息系统/遥感技术支持下顺向坡滑坡危险性评价研究。研究结果表明,根据模型概率值分布和已知滑坡发育关系,可以将研究区划分为高危险区、中等危险区、低危险区3个等级,高危险区包含70%已知滑坡,中等危险区包含14%已知滑坡,评价结果和实际滑坡发育情况吻合,合理地反映区内滑坡灾害发育的总体特征。     

滑坡防治工程效果的模糊综合后评价研究

通过现场调研比较,筛选出滑坡防治效果的11项后评价因子,包括滑坡本体因子和防治工程因子两大类。进而分析了国内外各种综合评价方法,由于人们对滑坡及治理工程的认识存在不确定性和模糊性,故尝试采用模糊数学方法进行定量或半定量描述,根据滑坡及防治工程特征对后评价效果的影响程度确定其隶属度,并提出修正的功能重要度系数权重确定法来确定权重,初次建立了模糊综合评价模型。     

基于GIS的云南小江流域滑坡因子敏感性分析

进行滑坡因子敏感性分析是滑坡研究的一个重要的步骤，有利于研究滑坡的变形破坏机理及分析滑坡的空间分布规律，采用滑坡确定性系数的方法对云南小江流域进行了影响因子敏感性分析，确定了有利于滑坡发育的岩性、结构、坡度、高程、断裂等影响因子。     

基于GIS的汶川地震滑坡灾害影响因子确定性系数分析

 2008年5月12日14时28分,四川省汶川发生了8.0级大地震,地震诱发了数以万计的滑坡灾害。在大约48 678 km2的区域内,采用震后航空像片与多源卫星影像解译并结合野外调查验证的方法,共圈定出48 007个地震滑坡灾害。在此基础上,选取地层、岩性、断裂、地震烈度、宏观震中、地表破裂调查点、地形坡度、坡向、顺坡向曲率、高程、水系与公路共12个影响因子作为汶川地震诱发滑坡影响因子,利用GIS强大的空间分析能力与确定性系数方法,对这12个影响因子进行敏感性研究。研究结果表明：(1) 寒武与震旦系是地震滑坡易发地层,侵入岩组、灰岩为主的岩组是地震滑坡发育的高敏感性岩组;(2) 地震滑坡受中央断裂影响最大,同时还受控于前山断裂,受后山断裂的影响较小;(3) 地震滑坡易发性分别随着地震烈度、与震中的距离、与地表破裂点距离的增加而减少;(4) 坡度大于40°是地震滑坡的易发坡度,E,ES方向为地震滑坡的易发坡向,高程范围为1 000～2 000 m,尤其是高程1 000～1 500 m范围为地震滑坡易发区;(5) 400 m水系缓冲区和2 000 m公路缓冲区范围内滑坡易发性较高。确定研究区内各地震滑坡影响因子最利于滑坡发生的数值区间,为进一步地震滑坡区域评价及预测奠定基础。     

基于ANN和LR的远安县滑坡易发性评价

在人工智能算法快速发展的背景下,选取人工神经网络模型(ANN)和逻辑回归模型(LR)对湖北省远安县进行滑坡易发性评价,得到滑坡易发性区划图,并对结果进行对比分析。该区共发育滑坡177处,提取出了与滑坡发生相关的9类指标因子。利用相关性分析,剔除高程因子,选择其余8类因子用于滑坡易发性评价,利用Arc GIS和SPSS Modeler软件得到研究区滑坡易发性区划图。最后,利用ROC曲线图对两个模型的成功率进行分析,得到人工神经网络模型和逻辑回归模型的AUC值分别为0.864和0.809,说明人工神经网络模型在该研究区的预测能力较好。     

中高山地区滑坡形成主控因子分析

滑坡的形成受内部静态物质因子和外部动态诱发因子的共同作用,分析各个因子的主次关系,对研究滑坡形成机理和滑坡预警预报及治理工作具有重要意义.在调查分析铜仁地区51个滑坡的斜坡形态、斜坡坡度、斜坡高差、滑床岩性、基岩倾角、滑床结构、滑体厚度、滑面倾角等8个影响因子的基础上,运用主成份分析方法确定滑坡发育影响因子的权重,建立由主控因子确定的铜仁地区滑坡发育评价模型.研究表明,斜坡形态、滑面倾角、斜坡坡度、斜坡高差、滑床岩性、基岩倾角等6个因素为铜仁地区滑坡形成的主控因素,其中滑床特征因子(滑床岩性、基岩倾角、滑床结构)为第一控制因素,地形地貌因素(斜坡形态、斜坡坡度、斜坡高差)为第二控制因素,滑带特征因素(滑面倾角)为第三控制因素.     

汶川县地震滑坡易发性LR与NN评价比较研究

通过地理信息系统(GIS)技术,结合神经网络(NN)和逻辑回归模型(LR)开展汶川县范围内地震诱发滑坡易感性评价,并对两种模型结果进行比较研究。基于2008年5.12 Ms8.0级地震,选取高程、坡度、坡位、坡向、岩性、微地貌、距断层距离、距水系距离、距道路距离、年平均降雨量、归一化植被指数、地震峰值加速度共12个因子作为地震滑坡影响因子,基于ARCGIS10.1平台将这些影响因子专题图层栅格化;采用提取的模型训练样本,由R软件对神经网络(NN)和逻辑回归模型(LR)进行训练;将训练好的模型对整个汶川县地震滑坡易感性进行仿真,并将仿真结果划分为五类滑坡敏感区域:极低,低,中,高和极高,分别得到LR与NN模型仿真的滑坡易发性分区图;根据汶川县实际地震滑坡分布图进行统计分析,以及采用ROC曲线对两种模型的仿真结果进行对比分析,神经网络(NN)和逻辑回归模型(LR)的AUC值分别为0.930和0.941。研究表明两种模型的滑坡易感性评价图与实际滑坡发育基本吻合,评价结果较好,且LR模型预测精度相对较高。     

基于滑坡分类和加权频率比模型的滑坡易发性评价

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。     

汶川地震诱发滑坡与地震动峰值加速度对应关系研究

 利用汶川地震获得的地震动记录及峰值加速度数据和收集整理的近3 000个崩滑点数据,对汶川地震诱发滑坡与地震动峰值加速度之间的对应关系进行研究,得到如下认识：(1) 地震动峰值加速度与地震诱发崩滑之间存在非常明显的正相关性,随峰值加速度增加,地震滑坡灾害也逐渐严重。(2) 在龙门山震区存在0.2 g的峰值加速度分界线,大于此值时,地震滑坡灾害比较严重。(3) 整个区域峰值加速度的下限为0.05～0.07 g,小于此值时,诱发滑坡的可能性很小。(4) 不同地质区域对应斜坡临界加速度有所不同,一般在0.05～0.15 g之间变化,平均为0.1 g,说明震区斜坡承受地震的水平较差;峰值加速度超过局部场地斜坡临界加速度后,诱发滑坡的可能性增加。得到的峰值加速度研究结果与其他研究结果较为一致,表明利用地震动参数研究地震滑坡具有很好的一致性。依据峰值加速度与地震滑坡的对应关系,可以对震后滑坡灾害作快速评估,也可以将其应用到地震滑坡灾害预测/区划工作中,与地震动峰值加速度区划形成很好的衔接。     

C4.5算法和GIS技术在波密县滑坡灾害风险评估中的应用

波密县地处西藏东南部,地貌地质环境特殊,滑坡灾害频发.本文选取地层岩性、边坡高度、边坡角度、坡面走向、水系距离和植被覆盖度作为评价因子,通过ArcGIS提取滑坡灾害评价因子相关数据,并利用C4.5算法建立的决策树分类模型对研究区域滑坡灾害的风险性进行了定量评估.结果表明:该模型总体预测精度达到89.7％,有效预测精度高...     

Landslide hazard zonation of the Khorshrostam area, Iran

 Landslide hazard zonation is a method to evaluate the risk where there is the potential for landslides. The factors contributing to the hazard in an area can usually be identified, results of the investigations frequently being presented as a landslide hazard zonation map indicating zones of similar risk of the occurrence of a landslide. Korshrostam is one of the areas most susceptible to landslides in Iran with more than 13% of its surface being affected by landslide activity. The effects include damage or disturbance to villages, farmlands and roads as well as the exacerbation of erosion of the land surface and consequently an increase in the rate of sedimentation in the water flowing into the reservoir of the Manjil dam. The method of landslide zonation used in this study was based on a simple grid unit. A number of factors contributing to the likelihood of landsliding were considered, including lithology, slope, tectonic activity, land use and groundwater. For each grid unit, the incidence of landsliding and an assessment of the likely contributory factors were recorded in terms of a surface percentage index (SPI). A computer program was written using fuzzy sets to calculate the hazard potential index (HPI) for each unit. This was used to prepare the landslide hazard zonation map. Received: 10 June 1999 · Accepted: 16 September 1999     

滑坡稳定性综合区划研究

滑坡的稳定性分区和影响因子分析是工程治理区划的关键问题。构建模糊聚类–灰色关联综合决策模型,可实现其稳定性综合区划研究。基于官家滑坡实际地质条件,进行稳定性模糊聚类分区,并结合详细地质调查分析和滑坡属性特征研究将其整体分为深层易滑区和浅层坍塌区,即碎石土深层滑动区和含碎石黏土浅层滑坡及局部坍塌区;再对各区构建影响因子决策矩阵进行灰色关联分析,判决各因子对滑坡稳定性的影响程度。结果表明:易滑区主要影响因子是滑体重量、地下水、下滑推力,坍塌区主要影响因子是下滑推力,并且经工程治理评价证实综合决策模型对滑坡工程治理区划具有十分重要的指导意义。     

Factor analysis for the statistical modeling of earthquake-induced landslides

Earthquake-induced landslides are difficult to assess and predict owing to the inherent unpredictability of earthquakes. In most existing studies, the landslide potential is statistically assessed by collecting and analyzing the data of historical landslide events and earthquake observation records. Unlike rainfall-induced landslides, earthquake-induced landslides cannot be predicted in advance using real-time monitoring systems, and the development of the models for these landslides should instead depend on early earthquake warnings and estimations. Hence, in this study, factor analysis was performed and the frequency distribution method was employed to investigate the potential risk of the landslides caused by earthquakes. Factors such as the slope gradient, lithology (geology), aspect, and elevation were selected and classified as influential factors to facilitate the construction of a landslide database for the area of study.     

滑坡的易滑度分区及其概率预报模式

滑坡的发生主要由地形、地质和降雨三方面因素决定。基于区域地质-气象耦合分析的思路,有可能提高区域性滑坡的预报精度。首先通过将降雨条件和地质环境条件相结合的方法,提出一种新的滑坡概率预报模式,利用数理统计分析,提出利用最大24h雨强和前15d实效降雨量作为滑坡灾害发生的短期预报判据;然后以重庆市区作为研究对象,选择岩性组合、地形坡度、边坡形态、岩体结构和水文地质五大因素及其21种状态为预测变量,利用信息量法进行了易滑度的分区;最后,对概率预报滑坡的可行性进行了实例分析和探讨。     

Identification of landslide hazard and risk ‘hotspots’ in Europe

Landslides are a serious problem for humans and infrastructure in many parts of Europe. Experts know to a certain degree which parts of the continent are most exposed to landslide hazard. Nevertheless, neither the geographical location of previous landslide events nor knowledge of locations with high landslide hazard necessarily point out the areas with highest landslide risk. In addition, landslides often occur unexpectedly and the decisions on where investments should be made to manage and mitigate future events are based on the need to demonstrate action and political will. The goal of this study was to undertake a uniform and objective analysis of landslide hazard and risk for Europe. Two independent models, an expert-based or heuristic and a statistical model (logistic regression), were developed to assess the landslide hazard. Both models are based on applying an appropriate combination of the parameters representing susceptibility factors (slope, lithology, soil moisture, vegetation cover and other- factors if available) and triggering factors (extreme precipitation and seismicity). The weights of different susceptibility and triggering factors are calibrated to the information available in landslide inventories and physical processes. The analysis is based on uniform gridded data for Europe with a pixel resolution of roughly 30 m × 30 m. A validation of the two hazard models by organizations in Scotland, Italy, and Romania showed good agreement for shallow landslides and rockfalls, but the hazard models fail to cover areas with slow moving landslides. In general, the results from the two models agree well pointing out the same countries with the highest total and relative area exposed to landslides. Landslide risk was quantified by counting the number of exposed people and exposed kilometers of roads and railways in each country. This process was repeated for both models. The results show the highest relative exposure to landslides in small alpine countries such as Lichtenstein. In terms of total values on a national level, Italy scores highest in both the extent of exposed area and the number for exposed population. Again, results agree between the two models, but differences between the models are higher for the risk than for the hazard results. The analysis gives a good overview of the landslide hazard and risk hotspots in Europe and allows a simple ranking of areas where mitigation measures might be most effective.     

Application of GIS-based statistical modeling for landslide susceptibility mapping in the city of Azazga,Northern Algeria

Landslide susceptibility mapping is a necessary tool in order to manage the landslides hazard and improve the risk mitigation. In this research, we validate and compare the landslide susceptibility maps (LSMs) produced by applying four geographic information system (GIS)-based statistical approaches including frequency ratio (FR), statistical index (SI), weights of evidence (WoE), and logistic regression (LR) for the urban area of Azazga. For this purpose, firstly, a landslide inventory map was prepared from aerial photographs and high-resolution satellite imagery interpretation, and detailed fieldwork. Seventy percent of the mapped landslides were selected for landslide susceptibility modeling, and the remaining (30%) were used for model validation. Secondly, ten landslide factors including the slope, aspect, altitude, land use, lithology, precipitation, distance to drainage, distance to faults, distance to lineaments, and distance to roads have been derived from high-resolution Alsat 2A satellite images, aerial photographs, geological map, DEM, and rainfall database. Thirdly, we established LSMs by evaluating the relationships between the detected landslide locations and the ten landslides factors using FR, SI, LR, and WoE models in GIS. Finally, the obtained LSMs of the four models have been validated using the receiver operating characteristics curves (ROCs). The validation process indicated that the FR method provided more accurate prediction (78.4%) in generating LSMs than the SI (78.1%),WoE (73.5%), and LR (72.1%) models. The results revealed also that all the used statistical models provided good accuracy in landslide susceptibility mapping.

     

A method for quick assessment of earthquake-triggered landslide hazards: a case study of the Mw6.1 2014 Ludian,China earthquake

Rapid assessment of the distribution of earthquake-triggered landslides is an important component of effective disaster mitigation. The effort should be based on both seismic landslide susceptibility and the ground shaking intensity, which is usually measured by peak ground acceleration (PGA). In this paper, we address this issue by analyzing data from the Mw6.1 2014 Ludian, China earthquake. The Newmark method of rigid-block modeling was applied to calculate the critical acceleration of slopes in the study area, which serve as measurement of slope stability under seismic load. The assessment of earthquake-triggered landslide hazard was conducted by comparing these critical accelerations with the distribution of known PGA values. The study area was classified into zones of five levels of landslide hazard: high, moderate high, moderate, light, and very light. Comparison shows that the resulting landslide hazard zones agree with the actual distribution of earthquake-triggered landslides. Nearly 70% of landslides are located in areas of high and moderately high hazard, which occupy only 17% of the study region. This paper demonstrates that using PGA, combined with the analysis of seismic landslide susceptibility, allows a reliable assessment of earthquake-triggered landslides hazards. This easy-operation mapping method is expected to be helpful in emergency preparedness planning, as well as in seismic landslide hazard zoning.

     

基于GIS技术的滑坡群影响因子分析与评价

GIS技术在滑坡评价中得到了广泛应用,为解决滑坡的影响因子识别和危险性预测等提供了有效手段.本次研究借助GIS技术平台,建立三峡库区某滑坡群空间分布模式、识别滑坡发育影响因子,判定滑坡发育阶段,最后基于GIS技术评价滑坡稳定性.研究结果表明,GIS能够有效快速的解决滑坡评价的有关问题,且该滑坡群发生的主要影响因子是岩土...