强度折减有限元法在滑坡特性预测的应用探讨

滑坡类型、规模、滑动面的位置及其影响距离等滑坡特性的有效预测,将有助于规划合适的减灾工作以减少滑坡灾害所带来的损失。利用强度折减有限元法与非饱和非稳定渗流有限元分析相结合的流固准耦合方法对滑坡模型试验进行了数值分析,结果表明:该法在描述降雨条件下边坡变形至失稳过程等方面具有明显的优势,即可用以预测滑坡的类型与规模以及潜在滑动面的位置,并可对滑坡的影响距离进行合理的定性分析与描述,因此,该法对滑坡减灾工作具有实用价值。     

Mapping the susceptibility of rainfall and earthquake triggered landslides along China–Nepal highways

The 2015 Gorkha earthquake (Mw?=?7.8) caused significant earthquake triggered landslides (ETL) in a landscape that is heavily intervened by rainfall triggered landslides (RTL). China’s Belt and Road Initiative plan to boost South-Asian regional trade and mobility through two key highway corridors, i.e. 1) Longmu–Rasuwa–Kathmandu (LRK) and 2) Nyalam–Tatopani–Kathmandu (NTK) route, that dissect the Himalayas through this geologically unstable region. To understand the spatial characteristics and susceptibility of these ETL and RTL, we delineate the landslides by means of time variant satellite imageries, assess their spatial distribution and model their susceptibilities along the highway slopes. We use a coupled frequency ratio (FR) – analytical hierarchy process (AHP) model by considering nine landslide determinants, e.g. geomorphic type (slope, aspect, curvature, elevation), hydrologic type (erosive potential of gullies, i.e. stream power index and distance to streams), normalized difference vegetation index, lithology and civil structure type (i.e. distance to roads). The results demonstrate that elevation and slope predominantly control both these landslide occurrences. The model predicts locations of ETL with higher accuracy than RTL. On comparison, NTK was safer with 133.5 km² of high RTL or ETL (or both) landslide susceptible areas, whereas LRK has 216.04 km². For mapping the extent of these landslides, we constricted it to the slope units of highways to reduce the computational effort, but this technique successfully achieved an acceptable threefold average model prediction rate of 82.75% in ETL and 77.9% in RTL. These landslide susceptibility maps and route comparisons would provide guidance towards further planning, monitoring, and implementing landslide risk mitigation measures for the governments.

     

降雨型滑坡预报新方法

详细研究了三峡地区部分县市的滑坡和降雨历史资料，从滑坡与降雨量、暴雨以及降雨时间3个不同角度的关系分析了降雨与降雨型滑坡的关系。在此基础上，提出了降雨因子的概念。同时，还提出了一种预报降雨型滑坡的新方法，定量化地描述了降雨型滑坡的易发程度。按照一定的标准，对每种降雨分因子进行分级，通过多因子叠合分析来研究降雨因子与降雨型滑坡之间的关系，并据此准确地预报滑坡的易发程度。通过将这种滑坡预报新方法应用于三峡的万县地区，证明了它可以比较准确地确定滑坡发生的时间。这种滑坡预报方法将为根据历史降雨和滑坡资料来预测降雨型滑坡奠定良好基础。     

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。     

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.

     

Spatial prediction of landslide susceptibility using data mining-based kernel logistic regression,naive Bayes and RBFNetwork models for the Long County area (China)

The main goal of this study is to assess and compare three advanced machine learning techniques, namely, kernel logistic regression (KLR), naïve Bayes (NB), and radial basis function network (RBFNetwork) models for landslide susceptibility modeling in Long County, China. First, a total of 171 landslide locations were identified within the study area using historical reports, aerial photographs, and extensive field surveys. All the landslides were randomly separated into two parts with a ratio of 70/30 for training and validation purposes. Second, 12 landslide conditioning factors were prepared for landslide susceptibility modeling, including slope aspect, slope angle, plan curvature, profile curvature, elevation, distance to faults, distance to rivers, distance to roads, lithology, NDVI (normalized difference vegetation index), land use, and rainfall. Third, the correlations between the conditioning factors and the occurrence of landslides were analyzed using normalized frequency ratios. A multicollinearity analysis of the landslide conditioning factors was carried out using tolerances and variance inflation factor (VIF) methods. Feature selection was performed using the chi-squared statistic with a 10-fold cross-validation technique to assess the predictive capabilities of the landslide conditioning factors. Then, the landslide conditioning factors with null predictive ability were excluded in order to optimize the landslide models. Finally, the trained KLR, NB, and RBFNetwork models were used to construct landslide susceptibility maps. The receiver operating characteristics (ROC) curve, the area under the curve (AUC), and several statistical measures, such as accuracy (ACC), F-measure, mean absolute error (MAE), and root mean squared error (RMSE), were used for the assessment, validation, and comparison of the resulting models in order to choose the best model in this study. The validation results show that all three models exhibit reasonably good performance, and the KLR model exhibits the most stable and best performance. The KLR model, which has a success rate of 0.847 and a prediction rate of 0.749, is a promising technique for landslide susceptibility mapping. Given the outcomes of the study, all three models could be used efficiently for landslide susceptibility analysis.

     

滑坡空间预测中信息量模型的改进及应用

确定未来可能发生滑坡的区域,即滑坡空间预测对城乡土地规划具有重要的指导意义.针对国内在滑坡空间预测中应用较多的信息量模型,通过理论推导表明其使用前提是各影响因素相互独立,以一简单算例说明了在滑坡空间预测中因素间相关性对预测结果的影响,并建议引入因子分析评估和减少各因素间的相关性.将基于因子分析的信息量模型应用于某一流域...     

Clarifying the hydrological mechanisms and thresholds for rainfall-induced landslide: in situ monitoring of big data to unsaturated slope stability analysis

The development of early warning systems for landslide hazards has long been a challenge because the accuracy of such systems is limited by both the complicated underlying mechanisms of landslides and the lack of in situ data. In this study, we implemented a multivariate threshold criterion that integrates in situ monitoring data and data from unsaturated hydro-mechanical analyses as an early warning system for rainfall-induced landslides in the Wenchuan earthquake region of China. The results indicate that rainfall intensity is closely correlated with the probability of landslide occurrence. Variations in matric suction and suction stress were obtained from in situ measurements and used to quantify the soil water retention curve, which presented clear hysteresis characteristics. The impacts of rainfall infiltration on slope failure in post-earthquake landslide areas under transient rainfall conditions were quantified by hydro-mechanical modelling theories. Variations in the suction stress of unsaturated soil were used to calculate the safety factor. The influence of hydrological hysteresis processes on the slope failure mechanism was analysed. Multivariate threshold criteria that include the intensity–probability (I-P) threshold, soil moisture and matric suction based on in situ big data and unsaturated slope stability analysis benchmarks are proposed for use in an early warning system for rainfall-induced landslides.

     

基于GIS区域边坡失稳灾害预测与评价

 滑坡和泥石流是边坡失稳后两种主要的运动方式,是山区重大的地质灾害。对既往滑坡和泥石流进行研究,以此作为基础来预测和评价本地区潜在的滑坡和泥石流灾害,是防灾减灾的一个重要措施。大多数泥石流是在强降雨的情况下,由滑坡滑入山谷河道而形成的。基于地理信息系统(GIS)和数值模型相结合的方法,采用两步骤方法预测和评价日本熊本县水俣市宝川区集地区的滑坡和泥石流灾害。首先分析该区域可能存在的新滑坡,然后假定这些滑坡在遇到强降雨时形成泥石流,利用数值模拟流动过程分析其在三维复杂地形下的泛滥过程,预测可能受害的房屋和路段。     

降雨滑坡多指标监测预警方法研究

降雨滑坡是发生最频繁,损失最严重的地质灾害,但如何实现其准确预警仍是目前所面临的主要挑战。而探究降雨滑坡的物理机制是开展预警的关键突破口。结合典型震后滑坡5个水文年的长时间序列实时监测数据和非饱和水-力耦合分析方法,开展都江堰银洞子沟滑坡现场实测数据的物理机制研究及长时间序列边坡稳定性分析。基于非饱和边坡稳定性分析方法,结合长时间序列实测大数据的实时计算和分析,提出基于降雨强度-概率(I-P)、饱和度、基质吸力、地表倾斜角度和稳定性实时计算分析的多参数指标预警方法体系,基于多参数预警体系和实时监测数据,分别于2017年8月和2018年6月在银洞子沟两次实现成功临灾预警,成功临灾预警实例验证了多参数阈值预警模型的可靠性和实用性,研究旨在为降雨型滑坡的准确预警提供新的参考和预警模式。     

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。     

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.     

Landslide susceptibility maps using different probabilistic and bivariate statistical models and comparison of their performance at Wadi Itwad Basin,Asir Region,Saudi Arabia

The purpose of the current study is to produce landslide susceptibility maps using different probabilistic and bivariate statistical approaches; namely, frequency ratio (FR), weights-of-evidence (WofE), index-of-entropy (IofE), and Dempster–Shafer (DS) models, at Wadi Itwad, Asir region, in the southwestern part of Saudi Arabia. Landslide locations were identified and mapped from interpretation of high-resolution satellite images, historical records, and extensive field surveys. In total, 326 landslide locations were mapped using ArcGIS and divided into two groups; 75 % and 25 % of landslide locations were used for training and validation of models, respectively. Twelve layers of landslide-related factors were prepared, including altitude, slope degree, slope length, topography wetness index, curvature, slope aspect, distance from lineaments, distance from roads, distance from streams, lithology, rainfall, and normalized difference vegetation index. The relationships between the landslide-related factors and the landslide inventory map were calculated using different statistical models (FR, WofE, IofE, and DS). The model results were verified with landslide locations, which were not used during the model training. In addition, receiver operating characteristic curves were applied, and area under the curve (AUC) was calculated for the different susceptibility maps using the success (training data) and prediction (validation data) rate curves. The results showed that the AUC for success rates are 0.813, 0.815, 0.800, and 0.777, while the prediction rates are 0.95, 0.952, 0.946, and 0.934 for FR, WofE, IofE, and DS models, respectively. Subsequently, landslide susceptibility maps were divided into five susceptibility classes, including very low, low, moderate, high, and very high. Additionally, the percentage of training and validating landslides locations in high and very high landslide susceptibility classes in each map were calculated. The results revealed that the FR, WofE, IofE, and DS models produced reasonable accuracy. The outcomes will be useful for future general planned development activities and environmental protection.     

基于GIS的区域滑坡危险性预测方法与初步应用

在GIS平台上，将多元空间信息统计分析方法与非线性统计预测方法相结合，在充分考虑滑坡与各环境因子之间的统计相关性和位置相关性的基础上，研究了滑坡与环境因子之间定量关系的表示方法，建立了单因子分析，多因子分析、整组性分析和多元空间信息的非线性预测模型，在理论基础上，选取香港大屿山岛中部作为试验研究区，利用环境因子进行区域滑坡危险性预测，经实际资料检验表明，该模型可获取较高的预测精度，具有极大的应用潜力。     

Assessing landslide susceptibility using Bayesian probability-based weight of evidence model

This study aims to demonstrate the application of a Bayesian probability-based weight of evidence model to map landslide susceptibility in the Tevankarai stream watershed, Kodaikkanal, India. Slope gradient, relief, aspect, curvature, land use, soil, lineament density, flow accumulation and proximity to roads were the landslide conditioning factors we considered in order to assess susceptibility. The weight of evidence model uses the prior probability of occurrence of a landslide event to identify areas prone to landslides based on the relative contributions of landslide conditioning factors. A pair-wise test of conditional independence was performed for the above factors, allowing the combination of conditioning factors to be analyzed. The contrast (difference of W ⁺ and W ^?) was used as weight for each factor’s type. The best observed combination consisted of the relief, slope, curvature, land use and distance to road factors, showing an accuracy of 86.1 %, while the accuracy of the map with all factors was 83.9 %.     

基于GIS的滑坡预测模型的预测率及其作用

滑坡预测模型的选择直接影响到滑坡预测的准确性,是滑坡预测的关键所在。该研究利用意大利Alpago地区的滑坡数据和其他相关地理空间数据,以模糊伽马模型、模糊代数积模型、模糊代数和模型以及模糊最小模型等4个定量滑坡预测模型为例,探讨滑坡预测模型的预测率在对比、评价和选择不同模型方面的作用。滑坡预测模型的预测率是,模型预测结果图的各个级别类型中,未用于建模的滑坡面积百分比的累积分布函数。在地理信息系统中,利用已知的滑坡分布数据和模型的预测结果图,可以计算滑坡预测模型的预测率。研究结果表明,滑坡模型的预测率是滑坡预测模型自身特性的度量,在输入图层和滑坡类型确定的条件下,滑坡预测模型的预测率可作为对比、评价和选择不同模型的定量指标,可以用来确定最合适的预测模型。     

基于关联维数的黄土滑坡空间分布结构及其成因分析

 为定量探究区域滑坡空间分布结构,揭示不同规模类型区域滑坡分布模式异同,分析其异同原因,拓深对区域滑坡空间分布规律的认知,在建立黄土滑坡详细编录数据库的基础上,借鉴粒度分析理论,引入滑坡径级概念。采用关联维数、核密度和GIS空间分析方法,定量地分析不同径级上区域黄土滑坡空间分布结构及其形成原因。结果表明：(1) 径级50～75 m黄土滑坡所占比例最大,占到滑坡总数的28.87%;径级＞100 m黄土滑坡所占比例较小,占到滑坡总数的15.12%;(2) 各径级区域滑坡空间结构具有多分形特征,在30 km尺度上存在明显的拐点;在小于30 km尺度上区域滑坡各径级的关联维数随着径级规模增大而增加,分布模式由聚集分布逐渐过渡到分散分布,在大于30 km尺度上区域滑坡都呈现聚集分布状态;(3) 成因分析表明,黄土滑坡在发育过程中,更多是受地形地貌的影响,在遴选的3个分布模式主要影响因素中,距河流距离对区域黄土滑坡分布模式的影响最大,其次为地形起伏度,小规模滑坡在距河流距离近(地形起伏度小)的地方所占比例较高,而大规模滑坡在距河流距离远(地形起伏度大)的地方所占比例较高。坡度虽然是影响滑坡稳定性的重要影响因子,但对于分布模式的影响几乎没有。     

水库滑坡变形特征和预测预报的数值研究

水库滑坡不同于一般山体滑坡,其稳定性受水位波动的影响十分明显。由于水库滑坡的特殊性和重要性,其变形特征和预测预报的研究已成为目前研究的热点和难点。库水水位的波动,改变了水库滑坡的水力边界条件,引起坡体内渗流场的非稳定渗流,采用传统的分析方法很难对其变形破坏特征和稳定性的动态变化进行研究,并对其失稳破坏进行预测预报。因此,基于数值极限分析方法对水位下降过程中滑坡体的变形特征和稳定性的动态变化规律进行了研究;通过位移-时间关系曲线以及位移-时间对数关系曲线对滑坡体的变形破坏特征进行了定量研究,并提出利用水平位移陡升段（加速段）和水平方向的夹角作为滑坡临滑预报的判据,为认清水库滑坡的破坏机制和提升其预报水平提供了新的思路。     

Landslide assessment for land use planning and infrastructure management in the Paphos District of Cyprus

The Paphos District has been described as one of the most landslide-prone areas of Cyprus, with landslides impacting villages, roads and other infrastructure. With increasing levels of development and investment in infrastructure, Cypriot authorities are investigating ways to assess landslide susceptibility, hazard and risk for planning purposes. A 2-year project has catalogued over 1,840 landslides, investigated the spatial distribution of key landslide attributes, and used the results to develop maps of landslide susceptibility across large areas of the Paphos District. To gain a better understanding of the materials and failure mechanisms involved, 20 of these landslides were selected for further study, including engineering geological mapping, ground investigation, laboratory testing, development of ground models and slope stability analysis at specific locations. The results enabled soil parameters to be reviewed, thus strengthening the interpretations derived from field observations. The use of the mapping outputs is discussed in terms of planning and engineering applications and recommendations are made for strengthening and expanding the landslide database.     

Assessment and validation of GIS-based landslide susceptibility maps: a case study from Feltrino stream basin (Central Italy)

Landslide susceptibility studies focus on producing susceptibility maps starting from landslide inventories and considering the main conditioning factors. The validity of susceptibility maps must be verified in terms of model accuracy and prediction skills. This paper deals with a GIS-based landslide susceptibility analysis and relative validation in a hilly-coastal test-area in Adriatic Central Italy. The susceptibility analysis was performed via bivariate statistics using the Landslide-Index method and a detailed (field-based) landslide inventory. Selection and mapping of conditioning factors and landslide inventories was derived from detail geomorphological analyses of the study area. The susceptibility map was validated using recent (shallow) landslides in terms of both model accuracy and prediction skills, via Success rate and Prediction rate curves, respectively. In addition, a pre-existing official landslide inventory was applied to the model to test whether it can be used when a detailed (field-based) inventory is not available, thereby extending its usability in similar physiographic regions. The outcome of this study reveals that slope and lithology are the main conditioning factor of landslides, but also highlights the key role of surficial deposits in susceptibility assessment, for both their type and thickness. The validation results show the effectiveness of the susceptibility model in both model accuracy and prediction skills given the good percentage of correctly classified landslides. Moreover, comparison of the susceptibility map with the official Regional landslides inventory proves the possibility of using the developed susceptibility model also in the absence of detailed landslide mapping, by considering inventories that are already available.