基于遥感影像多尺度分割与地质因子评价的滑坡易发性区划

区域滑坡易发性的研究是滑坡空间预测的核心内容之一。从影像多尺度分割和面向对象的分类理论出发,以研究区遥感影像的熵、能量、相关性、对比度共4个参数作为影像纹理因子提取易发性特征,利用滑坡所处区域的库水影响等级、坡度、斜坡结构、工程岩组4类地质因子分析地质背景,搭建C5.0决策树的易发性分类模型,实现了对研究区内4类滑坡易发性单元的预测。结果表明:高易发性单元的工程岩组通常发育为软岩岩组和软硬相间岩组,且坡度在15°～30°之间;模型显示该区域训练样本和测试样本平均正确率达91.64%,Kappa系数分别为0.84,0.51,因此这种基于影像多尺度分割与地质因子分级的滑坡易发性分类研究具有一定的适用性。     

基于GIS与ANN模型的地震滑坡易发性区划

基于遥感数据、地理信息系统(GIS)技术和人工神经网络(ANN)模型,开展地震滑坡易发性区划研究.2010年4月14日玉树地震后,基于航片与卫星影像目视解译,并辅以野外调查的方法,在地震区圈定了2036处地震诱发滑坡.选择高程、坡度、坡向、斜坡曲率、坡位、与水系距离、地层岩性、与断裂距离、与公路距离、归一化植被指数(NDVI)、与同震地表破裂距离、地震动峰值加速度(PGA)共12个因子作为地震滑坡易发性评价因子.这些因子均是应用GIS技术与遥感影像处理技术,基于地形数据、地质数据、遥感数据得到.训练样本中的滑动样本有两组,一组是滑坡区整个单滑坡体的质心位置,另一组是滑坡滑源区滑前的坡体高程最高的位置.应用这12个影响因子,分别采用这两组评价样本,基于ANN模型建立地震滑坡易发性索引图,基于GIS工具建立地震滑坡易发性分级图.分别应用训练样本中滑坡分布的点数据去检验各自的结果正确率,正确率分别为81.53％与81.29％,表明ANN模型是一种高效科学的地震滑坡易发性区划模型.     

基于多模型的滑坡易发性评价以甘肃岷县地震滑坡为例

2013年7月22日,甘肃省岷县漳县交界处发生了MS6.6级地震(岷县地震),本文以这次地震烈度Ⅷ度区为研究区,根据地震前后遥感影像解译出来的2330个地震滑坡数据,以坡度、坡向、水系、岩性和断层为因子图层,分别应用模糊逻辑法,信息量模型及Shannon熵改进的信息量模型,对研究区的地震滑坡易发性进行评价。结果表明: 1滑坡的高易发性地区位于研究区的中间部分,以及水系0～50m这一缓冲区范围内,离水系越近滑坡易发性等级越高; 2应用ROC曲线对3个模型的易发性评价结果进行比较,信息量模型和Shannon熵改进的信息量模型的AUC值分别为0.8488, 0.8502; 模糊逻辑模型的AUC值为0.7640,表明前两个模型的表现较好,而模糊逻辑模型相对来说表现一般; 3通过对比3个模型中各等级易发性所占的面积比例和各等级易发性中滑坡数目占总数比例,表明Shannon熵改进后的模型更适用于灾害风险评价以及应急风险管理等实际应用。     

基于证据权法的湖南省石门县皂市水库滑坡易发性评价

湖南省石门县皂市水库地区滑坡地质灾害频发,采用证据权法进行滑坡易发性评价可以为滑坡防治提供科学依据.本文首先以斜坡单元为基本制图单元,利用ArcGIS空间分析功能,结合历史滑坡灾害点实地复核数据、遥感影像、地形图、数字高程模型、地质图等数据,提取了坡度、坡形、斜坡高差、植被覆盖度、地层岩性、斜坡结构类型、断层缓冲距离、道路缓冲距离、河流缓冲距离等9个证据因子并划分证据层;然后基于证据权法分别计算各证据层权重值,生成了研究区滑坡易发性分区图,并进行了预测精度分析.结果表明:(1)研究区滑坡易发性可划分为高易发区、中易发区、低易发区、极低易发区4类,4类分区面积占比分别为7.5％、20.6％、54.9％、17.0％;(2)基于成功率曲线法得出分区准确率为84.7％,评价结果与灾害点分布较为吻合.     

基于GIS汕头潮阳区崩塌、滑坡地质灾害危险性评价

以潮阳区1:5万地质灾害详细调查数据为基础,选取地质灾害点坡度、坡向、地形起伏度、工程地质岩组、地质构造、土地利用类型等6个因素评价指标,采用信息量法获取研究区易发性,基于GIS空间分析功能将10年一遇降雨工况和易发性分析计算,得出地质灾害危险性分区图。研究区综合地质灾害高危险区面积明显大于单灾种评价结果,高危险区主要位于崩塌、滑坡较发育的碎裂岩区域;对提高区域地质灾害风险预测能力及综合防治水平具有实际意义。     

基于GIS的哀牢山戛洒镇小流域滑坡易发性评价

根据研究区的基本情况,选择坡度、坡向、地层岩性、距断层距离、降雨、土地利用等6个评价因子,采用滑坡灾害易发性评价的GIS与AHP耦合模型进行戛洒镇滑坡灾害易发性评价,并将滑坡灾害分为极高、高、中、低和极低易发区5个区域进行了滑坡灾害易发性评价结果分析,以期为后期的小流域滑坡风险评估研究服务。     

基于GIS的概率比率模型的滑坡易发性评价

以中国1∶50万区域环境地质调查(以地质灾害为主)、700个县(市)地质灾害调查与区划调查等(成果)资料为基础,选取地形起伏度、地貌类型、工程地质岩组、地震动峰值加速度、年平均降雨量、土地利用程度综合指数6个评价因子,采用概率比率模型,1 km×1 km评价单元,计算得到全国滑坡易发性评价指数图,并验证了结果的可靠性。进行了易发程度分区,最终得到高易发区、中易发区、低易发区和不易发区4个分区,完成了全国滑坡易发程度分区图。研究表明,概率比率模型方法可以客观、定量地评价滑坡易发性,适用于大区域易发性评价。     

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。     

滑坡易发性预测不确定性:环境因子不同属性区间划分和不同数据驱动模型的影响

对于滑坡易发性预测建模,连续型环境因子在频率比分析时的属性区间划分数量（attribute interval numbers,AIN）和不同易发性预测模型是两个重要不确定性因素.为研究这两个因素对建模的影响规律,以江西省上犹县为例,考虑5种连续型环境因子AIN划分（4、8、12、16及20）和5种数据驱动模型（层次分析法（analytic hierarchy process,AHP）、逻辑回归（logistic regression,LR）、BP神经网络（back-propagation neural network,BPNN）、支持向量机（support vector machine,SVM）和随机森林（random forest,RF））,总计25种不同工况下的滑坡易发性预测研究.再开展滑坡易发性指数的不确定性（包括精度评价和统计规律等）分析.结果表明:（1）对于同一模型,随着AIN值从4增加至8再到20时,易发性预测精度先逐渐提升,然后缓慢提升直至稳定;（2）对于同一AIN值,RF模型预测精度最高,其后依次为SVM、BPNN、LR和AHP模型;（3）在25种组合工况下,AIN=20和RF模型的预测精度最高,AIN=4和AHP模型精度最低,但在AIN=8和RF模型组合下的易发性建模效率较高且精度也较高;（4）更大的AIN值和更先进的机器学习模型预测出的滑坡易发性指数的不确定性相对较低,更符合实际的滑坡概率分布特征.在环境因子属性区间划分为8和RF模型工况下高效准确地构建滑坡易发性预测模型.      

基于有效降雨强度的滑坡灾害危险性预警

选取湖北省恩施地区1 000 km2区域作为典型研究区, 在全面分析该区域历史滑坡资料的基础上, 根据该区滑坡生成与地层岩性之间的关系, 将研究区地层划分为高、中、低3类易发性岩组.分岩组统计降雨监测数据与历史滑坡信息, 得出有效降雨强度与关键降雨持续时间的散点图, 由此确定不同滑坡发生概率的有效降雨强度阈值, 提出该区的滑坡灾害危险性预警判别模型.基于样本区统计数据建立滑坡预测指标体系, 运用GIS得出研究区域的滑坡空间易发性区划结果, 并根据不同易发岩组-有效降雨强度模型, 叠加滑坡灾害易发性分区结果与降雨危险性预警等级分级结果, 对研究区的滑坡灾害危险性进行了预测预警.结果表明: 不同易发岩组-有效降雨强度模型所得预警结果与实际情况吻合, 预警模型具有考虑全面和预警精度高的特点, 在实际预警中切实可用.      

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.     

Landslide susceptibility mapping by binary logistic regression, analytical hierarchy process, and statistical index models and assessment of their performances

The current research presents a detailed landslide susceptibility mapping study by binary logistic regression, analytical hierarchy process, and statistical index models and an assessment of their performances. The study area covers the north of Tehran metropolitan, Iran. When conducting the study, in the first stage, a landslide inventory map with a total of 528 landslide locations was compiled from various sources such as aerial photographs, satellite images, and field surveys. Then, the landslide inventory was randomly split into a testing dataset 70 % (370 landslide locations) for training the models, and the remaining 30 % (158 landslides locations) was used for validation purpose. Twelve landslide conditioning factors such as slope degree, slope aspect, altitude, plan curvature, normalized difference vegetation index, land use, lithology, distance from rivers, distance from roads, distance from faults, stream power index, and slope-length were considered during the present study. Subsequently, landslide susceptibility maps were produced using binary logistic regression (BLR), analytical hierarchy process (AHP), and statistical index (SI) models in ArcGIS. The validation dataset, which was not used in the modeling process, was considered to validate the landslide susceptibility maps using the receiver operating characteristic curves and frequency ratio plot. The validation results showed that the area under the curve (AUC) for three mentioned models vary from 0.7570 to 0.8520 $ ({\text{AUC}}_{\text{AHP}} = 75.70\;\% ,\;{\text{AUC}}_{\text{SI}} = 80.37\;\% ,\;{\text{and}}\;{\text{AUC}}_{\text{BLR}} = 85.20\;\% ) $ ( AUC AHP = 75.70 % , AUC SI = 80.37 % , and AUC BLR = 85.20 % ) . Also, plot of the frequency ratio for the four landslide susceptibility classes of the three landslide susceptibility models was validated our results. Hence, it is concluded that the binary logistic regression model employed in this study showed reasonably good accuracy in predicting the landslide susceptibility of study area. Meanwhile, the results obtained in this study also showed that the statistical index model can be used as a simple tool in the assessment of landslide susceptibility when a sufficient number of data are obtained.     

基于GIS的白龙江流域甘肃段滑坡易发性评价

在甘肃省白龙江流域地质灾害资料收集及现场调查的基础上, 统计分析了该区滑坡发育与地层岩性、坡度、坡向、高程、断裂、植被等因素之间的关系, 建立了白龙江流域滑坡易发性评价指标体系。采用基于GIS的层次分析法评价模型, 完成了滑坡易发性分区评价, 将研究区滑坡按易发程度划分为高易发区、中易发区、低易发区和极低易发区, 其中, 高易发区占研究区总面积的13.59%, 主要分布在断裂带、白龙江两侧以及软弱岩土体分布的区域; 中易发区占27.85%;主要分布在白龙江支流以及主要道路两侧的一定范围内; 低易发区占33.09%, 主要分布在海拔相对较高、植被覆盖度较高、基本上无断裂带通过的区域; 其余区域为极低易发区, 占25.46%。对比分析显示评价结果与实际滑坡发育情况吻合, 可以较好地反映区内滑坡灾害发育的总体特征。      

Use of GIS-based fuzzy logic relations and its cross application to produce landslide susceptibility maps in three test areas in Malaysia

Landslides are one of the most frequent and common natural hazards in Malaysia. Preparation of landslide susceptibility maps is one of the first and most important steps in the landslide hazard mitigation. However, due to complex nature of landslides, producing a reliable susceptibility map is not easy. For this reason, a number of different approaches have been used, including direct and indirect heuristic approaches, deterministic, probabilistic, statistical, and data mining approaches. Moreover, these landslides can be systematically assessed and mapped through a traditional mapping framework using geoinformation technologies. Since the early 1990s, several mathematical models have been developed and applied to landslide hazard mapping using geographic information system (GIS). Among various approaches, fuzzy logic relation for mapping landslide susceptibility is one of the techniques that allows to describe the role of each predisposing factor (landslide-conditioning parameters) and their optimal combination. This paper presents a new attempt at landslide susceptibility mapping using fuzzy logic relations and their cross application of membership values to three study areas in Malaysia using a GIS. The possibility of capturing the judgment and the modeling of conditioning factors are the main advantages of using fuzzy logic. These models are capable to capture the conditioning factors directly affecting the landslides and also the inter-relationship among them. In the first stage of the study, a landslide inventory was complied for each of the three study areas using both field surveys and airphoto studies. Using total 12 topographic and lithological variables, landslide susceptibility models were developed using the fuzzy logic approach. Then the landslide inventory and the parameter maps were analyzed together using the fuzzy relations and the landslide susceptibility maps produced. Finally, the prediction performance of the susceptibility maps was checked by considering field-verified landslide locations in the studied areas. Further, the susceptibility maps were validated using the receiver-operating characteristics (ROC) success rate curves. The ROC curve technique is based on plotting model sensitivity—true positive fraction values calculated for different threshold values versus model specificity—true negative fraction values on a graph. The ROC curves were calculated for the landslide susceptibility maps obtained from the application and cross application of fuzzy logic relations. Qualitatively, the produced landslide susceptibility maps showed greater than 82% landslide susceptibility in all nine cases. The results indicated that, when compared with the landslide susceptibility maps, the landslides identified in the study areas were found to be located in the very high and high susceptibility zones. This shows that as far as the performance of the fuzzy logic relation approach is concerned, the results appeared to be quite satisfactory, the zones determined on the map being zones of relative susceptibility.     

Landslide Susceptibility Mapping Using Fuzzy Logic System and Its Influences on Mainlines in Lashgarak Region,Tehran, Iran

Landslide susceptibility mapping is among the useful tools applied in disaster management and planning development activities in mountainous areas. The susceptibility maps prepared in this research provide valuable information for landslide hazard management in Lashgarak region of Tehran. This study was conducted to, first, prepare landslide susceptibility maps for Lashgarak region and evaluate landslide effect on mainlines and, second, to analyze the main factors affecting landslide hazard increase in the study area in order to propose efficient strategies for landslide hazard mitigation. A GIS-based multi-criteria decision analysis model (fuzzy logic) is used in the present work for scientific evaluation of landslide susceptible areas in Lashgarak region. To this end, ArcGIS, PCIGeomatica, and IDIRISI software packages were used. Eight information layers were selected for information analysis: ground strength class, slope angle, terrain roughness, normalized difference moisture index, normalized difference vegetation index, distance from fault, distance from the river, and distance from the road. Next, eight different scenarios were created to determine landslide susceptibility of the study area using different operators (intersection (AND), union (OR), algebraic sum (SUM), multiplication (PRODUCT), and different fuzzy gamma values) of fuzzy overlay approach. After that, the performance of various fuzzy operators in landslide susceptibility mapping was empirically compared. The results revealed the excellent consistency of landslide susceptibility map prepared using the fuzzy union (OR) operator with landslide distribution map in the study area. Eventually, the accuracy of landslide susceptibility map prepared using the fuzzy union (OR) operator was evaluated using the frequency ratio diagram. The results showed that frequency values of the landslides gradually increase from “low susceptibility” to high “susceptibility” as 88.34% of the landslides are categorized into two “high” and “very high” susceptibility classes, implying the satisfactory consistency between the landslide susceptibility map prepared using fuzzy union (OR) operator and landslide distribution map.     

基于要素分析和二元统计模型的区域滑坡危险等级制图——以国道212线陇南段为例

国道212线陇南段是我国地质灾害最发育的地区之一,绘制该区的滑坡危险等级地图对灾害管理和发展规划是极其必要的。基于滑坡的野外调查、机理研究和室内试验等工作,分析了滑坡与各种要素的相关性,选择控制滑坡的9个重要要素作为评价要素,利用GIS和二元统计的信息值模型和滑坡先验风险要素模型绘制了研究区的滑坡危险等级地图。最后,选用区内11个具有明显滑动位移的活动滑坡与滑坡危险等级地图比较,检验其可靠度。结果表明,活动的滑坡绝大部分都位于危险等级很高和高的范围内,说明两种模型的评价结果与研究区实际情况相吻合,同时也反映出信息值模型与实际情况更加相符。     

Landslide susceptibility zonation in Greece

The objective of this study is to perform a preliminary national-scale assessment of the landslide susceptibility in Greece using a landslide inventory derived from historical archives. The effects of controlling factors on landslide susceptibility combined with multivariate statistics have been evaluated using GIS aided mapping techniques. Thousand six hundred thirty-five landslide occurrences, mainly earth slides obtained from Public Authorities archives, covering a long time period were recorded and digitally stored using a spatial relational database management system. Ten landslide predisposing factors (predictors) were identified, while digital thematic maps on the spatial distribution of those factors were generated. The correlation between the landslide locations and predictor classes was analyzed by using the Landslide Relative Frequency. R-mode factor analysis was applied to study the interrelations between predictors (independent variables) while weighting coefficients were determined. Landslide susceptibility was derived from an algorithm which modeled the influence of predictors, and a susceptibility map was compiled. The landslide susceptibility map was verified using a data set of 375 new landslide locations. It is the first comprehensive attempt to illustrate the landslide susceptibility in the total country based on the interpretation of historical data only.     

Effectiveness of visually analyzing LiDAR DTM derivatives for earth and debris slide inventory mapping for statistical susceptibility modeling

Landslide inventories are the most important data source for landslide process, susceptibility, hazard, and risk analyses. The objective of this study was to identify an effective method for mapping a landslide inventory for a large study area (19,186 km²) from Light Detection and Ranging (LiDAR) digital terrain model (DTM) derivatives. This inventory should in particular be optimized for statistical susceptibility modeling of earth and debris slides. We compared the mapping of a representative set of landslide bodies with polygons (earth and debris slides, earth flows, complex landslides, and areas with slides) and a substantially complete set of earth and debris slide main scarps with points by visual interpretation of LiDAR DTM derivatives. The effectiveness of the two mapping methods was estimated by evaluating the requirements on an inventory used for statistical susceptibility modeling and their fulfillment by our mapped inventories. The resulting landslide inventories improved the knowledge on landslide events in the study area and outlined the heterogeneity of the study area with respect to landslide susceptibility. The obtained effectiveness estimate demonstrated that none of our mapped inventories are perfect for statistical landslide susceptibility modeling. However, opposed to mapping polygons, mapping earth and debris slides with a point in the main scarp were most effective for statistical susceptibility modeling within large study areas. Therefore, earth and debris slides were mapped with points in the main scarp in entire Lower Austria. The advantages, drawbacks, and effectiveness of landslide mapping on the basis of LiDAR DTM derivatives compared to other imagery and techniques were discussed.     

Assessment of landslide susceptibility for a landslide-prone area (north of Yenice, NW Turkey) by fuzzy approach

. Regional landslide susceptibility assessments pose complex problems. To solve these problems, numerous approaches, such as statistical analysis, geotechnical engineering approach, geomorphologic approach and fuzzy logic, have been employed. However, all the available methods for regional landslide susceptibility assessments have some uncertainties due to a lack of knowledge and variability. Minimizing these uncertainties provides realistic approaches. Use of the fuzzy logic approach to produce a landslide susceptibility map of a landslide-prone area in NW Turkey is the main purpose of the present study. For this purpose, the study includes five main stages, these being the preparation of a landslide inventory of the study area, the application of factor analysis, the extraction of fuzzy if-then rules, the use of a geographical information system, and the control of the reliability of the resulting landslide susceptibility map. Slope angle, slope aspect, land use, weathering depth, water conditions and topographical elevation were considered as landslide conditioning factors for the study area. A total of 23 if-then rules was extracted from the field data. Employing these rules, fuzzified index maps representing each parameter were obtained. Finally, combining these maps, the landslide susceptibility map of the area was prepared. When compared with the landslide susceptibility map, the landslides identified in the area were found to be located in the very high- and high-susceptibility zones. As far as the performance of the fuzzy approach for processing is concerned, the images appear to be quite satisfactory, the zones determined on the map being zones of relative susceptibility.