基于GIS和信息量的滑坡灾害易发性评价——以三峡库区万州区为例

 以滑坡灾害发育较多的三峡库区万州区为研究区,基于指标因素状态分级和因素相关性分析结果,选取坡度、坡向、坡体结构、地层岩性、地质构造、水的作用以及土地利用7项影响因素,以全区700多个滑坡灾害点为样本数据,依据各因素状态下发生的滑坡频率曲线和信息量曲线的突变点为等级划分的临界值来确定因素状态,并在此基础上建立易发性评价指标体系。基于GIS的栅格数据模型,应用信息量理论开展研究区易发性评价,研究结果表明：易发性高和较高的区域主要分布在土地利用总体规划中的建设用地、侏罗系中统上沙溪庙组第二、三段(J2s2,J2s3)、库水变动带和河网影响带以及万州城区。统计结果表明,处在高易发和较高易发区面积为1 210 km2,其中高易发区和较高易发区分别占研究区总面积的9.71%和25.9%,研究区易发性评价精度高达87%。本文完整的论述了县域滑坡灾害易发性评价的理论方法和技术路线,并以三峡库区万州区为例开展滑坡灾害易发性评价、结果分析以及预测精度评价等,为该区域滑坡灾害防治规划与预测预报提供技术支持,为全国范围内县域滑坡灾害易发性评价提供理论指导和技术参考。     

九寨沟县滑坡灾害易发性快速评估模型对比研究

通过地理信息系统(GIS)技术,采用信息量(I)、确定性系数(CF)、逻辑回归(LR)、逻辑回归–信息量(LR-I)和逻辑回归–确定性系数(LR-CF)耦合模型的快速评估方法对九寨沟县范围内滑坡灾害易发性评价,并对5种模型进行比较研究。基于历史资料、遥感解译和现场调查,获取九寨沟全县6205个滑坡灾害点作为样本数据库,选取海拔、坡度、坡向、地形曲率、剖面曲率、平面曲率、地形起伏度、地表粗糙度、地表切割度、地层岩性、距断层距离、PGA、降雨、距公路距离和距水系距离共计15项评价指标因子,基于GIS平台提取80%滑坡点作为训练样本,采用I,CF,LR模型建立九寨沟县滑坡灾害易发性评价体系,并将滑坡易发性划分为极低、低、中、高和极高。基于I,CF和LR模型,提出LR-I和LR-CF耦合模型,实现各评价指标因子二次逻辑回归计算,优化了九寨沟地区滑坡灾害易发性区划图。最后利用未参与训练的20%滑坡点作为检验样本,利用频率比和ROC曲线进行精度检验。结果表明:5种评价模型得到的滑坡的高易发区和极高易发区频率比值占总频率比值均超过85%,I,CF,LR,LR-I和LR-CF的AUC评价精度分别为0.762,0.756,0.788,0.838和0.836,表明5种模型均能较好评价九寨沟地区滑坡灾害易发性。LR-I和LR-CF模型与单一的I,CF模型相比能将滑坡易发性评价精度提高约8%;与单一LR模型相比,其精度提高约5%,说明LR-I和LR-CF模型的滑坡预测更优于单一的I,CF和LR模型,为快速建立评价指标体系和区域滑坡易发性提供了可靠途径。     

九寨沟县滑坡灾害易发性快速评估模型对比研究EI北大核心CSCD

通过地理信息系统(GIS)技术,采用信息量(I)、确定性系数(CF)、逻辑回归(LR)、逻辑回归–信息量(LR-I)和逻辑回归–确定性系数(LR-CF)耦合模型的快速评估方法对九寨沟县范围内滑坡灾害易发性评价,并对5种模型进行比较研究。基于历史资料、遥感解译和现场调查,获取九寨沟全县6205个滑坡灾害点作为样本数据库,选取海拔、坡度、坡向、地形曲率、剖面曲率、平面曲率、地形起伏度、地表粗糙度、地表切割度、地层岩性、距断层距离、PGA、降雨、距公路距离和距水系距离共计15项评价指标因子,基于GIS平台提取80%滑坡点作为训练样本,采用I,CF,LR模型建立九寨沟县滑坡灾害易发性评价体系,并将滑坡易发性划分为极低、低、中、高和极高。基于I,CF和LR模型,提出LR-I和LR-CF耦合模型,实现各评价指标因子二次逻辑回归计算,优化了九寨沟地区滑坡灾害易发性区划图。最后利用未参与训练的20%滑坡点作为检验样本,利用频率比和ROC曲线进行精度检验。结果表明:5种评价模型得到的滑坡的高易发区和极高易发区频率比值占总频率比值均超过85%,I,CF,LR,LR-I和LR-CF的AUC评价精度分别为0.762,0.756,0.788,0.838和0.836,表明5种模型均能较好评价九寨沟地区滑坡灾害易发性。LR-I和LR-CF模型与单一的I,CF模型相比能将滑坡易发性评价精度提高约8%;与单一LR模型相比,其精度提高约5%,说明LR-I和LR-CF模型的滑坡预测更优于单一的I,CF和LR模型,为快速建立评价指标体系和区域滑坡易发性提供了可靠途径。     

基于信息量与神经网络模型的滑坡易发性评价

针对神经网络模型进行滑坡易发性评价时,传统的随机选取非滑坡单元存在准确性不高的缺点,提出信息量与神经网络结合的易发性评价模型。以江西省上犹县为研究区,首先,基于上犹县滑坡编录与实际调查,选取坡度、高程、坡向、平面曲率、剖面曲率,植被指数(NDVI)、湿度指数(TWI)、距水系距离、距道路距离、土地利用等10个环境因子,其次利用信息量模型对上犹县进行易发性分区,得到上犹县易发性分区图。然后,从信息量模型得出的易发性分区中的低易发区选取非滑坡单元,与滑坡编录中的历史滑坡点组成测试集与训练集,输入神经网络中训练模型,再将上犹县所有栅格输入,预测上犹县栅格的滑坡概率。最后利用自然断点法在上犹县栅格滑坡概率进行分类,得到基于信息量与人工神经网络结合的上犹县易发性分区图。由易发性结果表明:单独的信息量模型的成功率曲线下面积AUC=0.7364,历史灾害点位于高易发区与较高易发区的灾害数占总灾害数的55.6%;基于信息量与神经网络模型的AUC=0.7874;历史灾害点位于高易发区与较高易发区的灾害数占总灾害数的85.8%。信息量–神经网络的评价模型比单独的信息量模型的评价精度提高了5.1%;高易发区与较高易发区所涵盖的灾害数占比高30.2%。信息量–神经网络模型有更好的评价精度,并且证明了在信息量模型中的极低易发区选取非滑坡点具有可行性。     

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。     

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。     

基于信息量法的金沙江峡谷段滑坡易发性评价

文章以云南省迪庆藏族自治州金沙江峡谷区为研究区域,进行滑坡易发性评价。选取坡度、起伏度、植被覆盖指数、地层岩性、构造作用、河流作用、人类工程活动、降雨量八个影响因子。以266个滑坡点为样本,基于信息量法和GIS平台,构建滑坡易发性分区评价指标体系,计算各个影响因子分级状态下的信息量值,绘制各影响因子分级栅格图,通过栅格计算器叠加得到总区域信息量值,采用自然断点法将其分为5级。采用成功率曲线法进行模型评价,曲线下面积(AUC值)为0.7768,准确性较好。     

异质集成学习在滑坡易发性评价中的对比研究

滑坡易发性评价可以为有关部门制定滑坡相关的防灾减灾政策提供技术支撑。目前机器学习已被广泛应用于滑坡易发性评价中，而不同的机器学习模型预测精度各异，为对比分析不同异质集成学习模型在滑坡易发性评价中的精度表现，该文以滑坡多发的甘肃省天水市与陕西省宝鸡市交界处为研究区，采用Stacking、Blending和加权平均三种异质集成学习模型，以随机森林、支持向量机和BP神经网络作为基学习器，对研究区进行滑坡易发性评价对比研究。通过使用准确率、Kappa系数以及ROC曲线指标对三种异质集成模型及基学习器进行模型验证和对比分析，结果表明，Stacking集成模型的各项指标都优于其他对比模型，验证了Stacking集成模型在滑坡易发性评价方面较其他对比模型具有更高的预测精度。     

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。     

基于逻辑回归模型的凉山州滑坡易发性评价

凉山州地质环境复杂,地质灾害频发,为了防止凉山州因灾致贫、因灾返贫,对凉山州滑坡灾害进行易发性评价。选择10个因子,分析因子与滑坡的分布规律,并建立逻辑回归模型对凉山州滑坡灾害进行易发性评价。结果表明：凉山州滑坡分布于高程1 800 m～2 300 m;坡度10°～30°;坡向为东方向;坡位为中坡;距道路距离和距断层距离为小于2 km,NDVI为0.7～0.8;TWI为2～4;岩组为碎屑岩;土地利用类型为耕地。滑坡极高易发区位于凉山州中部安宁河、则木河断裂带两侧和东部汉源-甘洛带、峨边-金阳断裂带两侧。     

降雨影响下的区域滑坡危险性动态评价研究——以三峡库区万州主城区为例

 降雨引发的滑坡具有区域性的群发效应,能够在短时间内造成大量的灾难性损失。基于此,提出一种可考虑不同降雨期影响的区域滑坡危险性评价方法。该方法以瞬态降雨入渗的区域斜坡稳定性计算模型为基础,将滑坡危险性定义为在一定持续降雨期内各栅格单元体失稳的概率。通过岩土体物理力学参数的不确定性进行各栅格单元体失稳概率的求解,继而获得区域内滑坡的危险性分布。基于ArcGIS软件开发出区域滑坡危险性动态评价工具。以三峡库区万州主城区为例,详细介绍危险性评价工具的数据处理过程以及参数选取方法,并以2种不同的降雨工况进行比较计算。现场斜坡稳定性的调查与计算结果的对比及统计分析表明：滑坡的危险性分布图与真实滑坡的稳定性情况基本一致,并在一定程度上反应了该地区斜坡稳定性的时空分布特征,测试并验证了评价工具的正确性。     

基于地理信息系统和信息量模型的滑坡危险性评价——以重庆万州为例

针对滑坡危险性评价，以重庆万州滑坡地质灾害为例，采用地理信息系统技术，选取高程、地层岩性、坡向、坡度、距离河流的远近、距离道路的远近、建筑物分布和到遂宁组和沙溪庙组地层的距离等8个指标作为评价因子，利用信息量模型对万州研究区的滑坡地质灾害进行危险性评价。评价结果表明，地理信息系统和信息量模型能够很好地为滑坡灾害的危险性研究服务，可用来解决过去地质灾害危险性评价中效率低、精度筹、费时、费力等问题，从而实现滑坡地质灾害的信息化和科学化。     

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。     

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.

     

基于聚类分析和支持向量机的滑坡易发性评价

在将支持向量机(support vector machine,SVM)等机器学习模型用于区域滑坡易发性评价时,大都随机或主观地选取非滑坡栅格单元,不能保证所选的非滑坡栅格单元是真正的"非滑坡"。为解决此问题,提出基于聚类分析和SVM的滑坡易发性评价模型。该模型首先用自组织映射(self-organizing mapping,SOM)神经网络对滑坡易发性进行聚类分析;然后从极低易发区中选择非滑坡栅格单元,确保所选非滑坡栅格单元是高概率的"非滑坡";最后采用SVM模型基于已知滑坡、所选非滑坡和环境因子对滑坡易发性进行评价。将提出的SOM-SVM模型用于三峡库区万州区滑坡易发性评价,并将得到的易发性结果与随机选取非滑坡的单独SVM模型结果做对比。结果显示SOM-SVM模型具有比单独SVM模型更高的成功率和预测率,表明SOM神经网络能更准确地选取非滑坡栅格单元。     

Exploring spatial non-stationarity in the relationships between landslide susceptibility and conditioning factors: a local modeling approach using geographically weighted regression

Landslide susceptibility is the likelihood of landslide occurrence, in a specific place and time. The identification of the potential relationships between landslide susceptibility and conditioning factors is very important towards landslide hazard mitigation. In this paper, we implement a local statistical analysis model geographically weighted regression, in two catchment areas located in northern Peloponnese, Greece. For this purpose, we examined the following eight conditioning factors: elevation, slope, aspect, lithology, land cover, proximity to the drainage network, proximity to the road network, and proximity to faults. Moreover, the relationship between these factors and landsliding in the study area is examined. The local statistical analysis model was also evaluated by finding its differences with the performance of a standard global statistical model logistic regression. The results indicated that the global statistical model can be enhanced by the application of a local model. The outputs of the proposed approach favored a better understanding of the factors influencing landslide occurrence and may be beneficial to local authorities and decision-makers dealing with the mitigation of landslide hazard.

     

GIS based statistical and physical approaches to landslide susceptibility mapping (Sebinkarahisar,Turkey)

The case study presents GIS-aided statistically and physically based landslide susceptibility mapping in the landslide-prone Avutmus district of Sebinkarahisar (Giresun, Turkey). Field investigations, analysis of geological data and laboratory tests suggested that two important factors have acted together to cause sliding: ground water pressures and toe erosion. Frequency ratio (FR) and stability index mapping (SINMAP) were used to create the landslide susceptibility maps based on a landslide inventory; distance from drainage systems, faults and roads; slope angle and aspect; topographic elevation and topographical wetness index; and vegetation cover. Validation of the models indicated high quality susceptibility maps with the more realistic results were obtained from the statistically based FR model.     

Enhancing the accuracy of rainfall-induced landslide prediction along mountain roads with a GIS-based random forest classifier

Along mountain roads, rainfall-triggered landslides are typical disasters that cause significant human casualties. Thus, to establish effective mitigation measures, it would be very useful were government agencies and practicing land-use planners to have the capability to make an accurate landslide evaluation. Here, we propose a machine learning methodology for the spatial prediction of rainfall-induced landslides along mountain roads which is based on a random forest classifier (RFC) and a GIS-based dataset. The RFC is used as a supervised learning technique to generalize the classification boundary that separates the input information of ten landslide conditioning factors (slope, aspect, relief amplitude, toposhape, topographic wetness index, distance to roads, distance to rivers, lithology, distance to faults, and rainfall) into two distinctive class labels: ‘landslide’ and ‘non-landslide’. Experimental results with a cross validation process and sensitivity analysis on the RFC model parameters reveal that the proposed model achieves a superior prediction accuracy with an area under the curve  of 0.92. The RFC significantly outperforms other benchmarking methods, including discriminant analysis, logistic regression, artificial neural networks, relevance vector machines, and support vector machines. Based on our experimental outcome and comparative analysis, we strongly recommend the RFC as a very capable tool for spatial modeling of rainfall-induced landslides.