Micromorphology of landslide soil——Case study on the Jibazi landslide in Yunyang in the Three Gorges Region, China

Introduction The studies on landslides in the Three Gorges Region were conducted, e.g., those on affecting factors and forming mechanism of landslide (YIN Kunlong et al. 1998, DENG Qinglu et al. 2000, CHEN Yongbo et al. 2003, XU Qiang et al. 2003), on ris…     

Risk assessment and landslide prevention design using numerical modelling——A case study in Qingliu,China

Numerous Quaternary deposits are existed in the mountainous areas of Southwest China,especially in the transition zone between the QinghaiTibet Plateau and the Sichuan Basin, where strong tectonic movements and frequent climatic changes increase the potential landslides. The possible deformation and failure process of potential landslides and their impacts on the surrounding environment are important research topics. Field investigation and monitoring indicate that the Qingliu landslide in Xiame...     

From landslide characterization to nature reserve management: The “Scialimata Grande di Torre Alfina” landslide Geosite(Central Apennines,Italy)

Italy is characterized by widespread geomorphological instability, among which landslides leave impressive marks on the landscape. Nevertheless, landslide bodies may represent key sites for thematic and educational itineraries, especially in protected areas, where their management becomes an important issue. Our study focuses on the "Monte Rufeno Nature Reserve"(Central Apennines, Italy), where iconic landslides are present. Here, the "Scialimata Grande di Torre Alfina" landslide(SGTA) is listed...     

Formation mechanism of a rainfall triggered complex landslide in southwest China

This case study is about a landslide that occurred after 4 days of heavy rainfall,in the morning of June 29,2012,in Cengong County,Guizhou Province of China,geographical coordinated 108°20′-109°03′E,27°09′-27°32′N,with an estimated volume of 3.3×106 m3.To fully investigate the landslide process and formation mechanism,detailed geotechnical and geophysical investigations were performed including borehole drilling,sampling,and laboratory tests coupled with monitoring of displacement.Also,a combined seepage-slope stability modeling was performed to study the behavior of the landslide.After the heavy rainfall event,the sliding process started in this area.The landslide development can be divided into different parts.The man-made fill area,spatially distributed in the south side of the landslide area with low elevations,slid first along the interface between the slope debris and the strongly weathered bedrock roughly in the EW direction.Consequently,due to severe lateral shear disturbance,the slope in the main sliding zone slid next towards the SW direction,along the sliding surface developed within the strongly weathered calcareous shale formation located at a depth of 25-35 m.This means it was a rainfall triggered deep-seated landslide.Finally,retrogressive failure of a number of upstream blocks occurred,which moved in more than one direction.The initial failure of the man-made fill area was the‘engine’of the whole instability framework.This artificial material with low permeability,piled up in the accumulation area of surface and sub-surface and destroyed the drainage capacity of the groundwater.The numerical modeling results agreed with the analysis results obtained from the laboratory and field investigations.A conceptual model is given to illustrate the formation mechanism and development process of the landslide.     

Study on stability and reinforcement of a landslide on Ji''''an-Dandong highway

This study clarified the failure mechanism of a landslide on Ji'an-Dandong highway,through the detailed analysis of its geological condition.Then based on the back analysis of the broken landslide,take limit equilibrium method to evaluate the stability of the potential landslide.The result shows that the landslide is lack of safety stock,so anchor rope is designed to reinforce the landslide.     

Early landslide mapping with slope units division and multi-scale objectbased image analysis——A case study in the Xianshui River basin of Sichuan,China

Previous studies on optical remote sensing mapping of landslides mainly focused on new landslides that have occurred, but little attention was paid to the early landslide due to its high concealment. In SAR technology, a prevalent method to detect early landslides, only can be used to identify the potential hazards of slow deformation. Therefore, it is necessary to explore new method of early landslides mapping by integrating all types of direct and indirect early features, such as cracks on slo...     

Spatial prediction of landslide for Badong County in the ThreeGorges reservoir district

By using the landslide risk evaluating model and the advantages of GIS technology in image processing and space analysis, the relative landslide hazard and risk evaluating system of the new county site of Badong is built up. The system is mainly consisted of four subsystems： Information management subsystem, hazard as- sessment subsystem, vulnerability evaluation subsystem and risk prediction subsystem. In the system, landslide hazard assessment, vulnerability evaluation, risk predictions are carried out automatically based on irregular units. At last the landslide hazard and risk map of the study area is compiled. During the whole procedure, Matter-Element Model, Artificial Neural Network, ancl Information Model are used as assessment models. This system provides an effective way for the landslide hazard information management and risk prediction of each district in the Reservoir of Three Gorge Project. The result of the assessment can be a gist and ensure for the land planning and the emigration project in Badong.     

Study on stability and reinforcement of a landslide on Ji＇an-Dandong highway

This study clarified the failure mechanism of a landslide on Ji＇ an-Dandong highway, through the detailed analysis of its geological condition. Then based on the back analysis of the broken landslide, take limit equilibrium method to evaluate the stability of the potential landslide. The result shows that the landslide is lack of safety stock, so anchor rope is designed to reinforce the landslide.     

Coseismic landslide sediment increased by the “9.5” Luding earthquake,Sichuan, China

The "9.5" Luding earthquake(Ms 6.8),which occurred on September 5,2022,has triggered thousands of landslides,and caused coseismic landslide sediment in the mountain basin to increase significantly.After the Luding earthquake,landslide sediment may continue to divert to channels,and increase the activity of debris flows.Importantly,the formation of debris flows can pose a major threat to infrastructure,lives and property.To better understand the landslide sediment that increased by the "9.5" Ludi...     

Analysis and modeling of slope stability in the Three-Gorges Dam reservoir (China) �� The case of Huangtupo landslide

The water level in the Three Gorges Dam reservoir is expected to change between the elevations of 145 m and 175 m, as a function of the flood control implementation and the intensity of the annual flood. As a matter of fact, the hydraulical and mechanical loadings, related to the water level modifications, will result in alterations in the slope stability conditions. The town of Badong (Hubei), of 20 000 inhabitants, is one of the towns which was submerged by the impoundment of the reservoir. As a consequence, the new town of Badong was constructed on a nearby site which appeared to be partly an unstable site. A part of this site corresponds to an old landslide, the Huangtupo landslide, the base of which had to be submerged by the water of the reservoir. The analysis of the Huangtupo landslide, taking into account various events scenarios, drainage and reinforcement measures and monitoring devices, allows to illustrate the general process implemented all along the reservoir in order to mitigate the landslide hazard.     

基于信息量模型和数据标准化的滑坡易发性评价

本文以北川曲山-擂鼓片区为研究区,将坡度、坡向、高程、地层、距断层的距离、距水系的距离和距道路的距离作为该区域滑坡易发性评价因子。采用信息量模型计算了各项评价因子的信息量值,并运用4种标准化模型对信息量值进行标准化处理。各评价因子的权重由层次分析法（AHP）确定。在GIS中将权重值和各评价因子的标准化信息量值,进行叠加计算得到区域滑坡总信息量值,并基于自然断点法对其进行重分类,将研究区划分为极高易发区、高易发区、中易发区、低易发区和极低易发区5级易发区。将基于4种标准化模型和信息量模型得到的滑坡易发性评价结果进行了对比分析,结果表明：基于最值标准化信息量模型的滑坡易发性评价结果的ROC曲线下面积AUC值为0.807,高于其余模型的AUC值,说明最值标准化信息量模型的滑坡易发性评价效果最好。极高易发区面积占研究区面积的20.03%,离断层和水系较近,主要分布地层为寒武系、志留系和三迭系。研究结果可为区内滑坡风险评价和灾害防治提供参考。     

支持向量机与Newmark模型结合的地震滑坡易发性评估研究

Newmark位移模型是研究地震滑坡易发性的经典模型,机器学习方法支持向量机模型也越来越多的应用到滑坡易发性评估研究。本文将Newmark位移模型与支持向量机模型相结合,建立基于物理机理的地震滑坡易发性评估模型并应用于2008年汶川地震重灾区汶川县。从震后遥感影像目视解译出汶川县1900处地震诱发滑坡,并将其随机划分为70%的训练数据集和30%的验证数据集。选择地形起伏度、坡度、地形曲率、与构造断裂带距离、与水系距离、与道路距离6个因子与Newmark位移值共同作为地震滑坡易发性影响因素。利用ROC曲线和模型不确定性等指标对模型结果进行评估,并与二元统计模型频率比和多元统计模型Logistic回归的结果进行对比。结果表明：与频率比和Logistic回归模型相比,支持向量机模型的正确率最高,训练集和验证集ROC曲线下的面积分别为0.876和0.851。将模型应用于绘制汶川县地震滑坡易发性图,结果显示滑坡易发性图与实际的滑坡点位分布一致性较高,有80.4%的滑坡位于极高和高易发区。这说明支持向量机与Newmark位移方法结合建立的地震滑坡易发性评估模型有较高的预测价值,可以为滑坡风险评估和管理提供依据。     

基于贝叶斯优化机器学习超参数的滑坡易发性评价

利用机器学习模型进行滑坡易发性评价时, 不同的超参数设置往往会导致评价结果的不同。采用贝叶斯算法对4种常见机器学习模型(逻辑回归LR、支持向量机SVM、人工神经网络ANN和随机森林RF)的超参数进行了优化, 探索了该算法对滑坡易发性机器学习模型的优化效果。以湘中地区4县(安化县、新华县、桃江县和桃源县)滑坡易发性评价为例说明该算法的可行性与适用性。基于滑坡历史编录, 确定研究区内1 017个滑坡点, 并选定15个滑坡影响因子, 以此构建滑坡易发性模型的训练集和测试集。利用贝叶斯优化算法对4种机器学习模型的主要超参数进行了优化, 依据优化后的超参数建立了4种优化模型, 并使用AUC值等指标来比较其预测能力。结果表明: 经超参数优化后的4种机器学习模型预测性能均有所提高, 且基于贝叶斯优化的随机森林模型表现最好。      

Regional scale rainfall- and earthquake-triggered landslide susceptibility assessment in Wudu County, China

Wudu County in northwestern China frequently experiences large-scale landslide events.High-magnitude earthquakes and heavy rainfall events are the major triggering factors in the region.The aim of this research is to compare and combine landslide susceptibility assessments of rainfalltriggered and earthquake-triggered landslide events in the study area using Geographical Information System(GIS) and a logistic regression model.Two separate susceptibility maps were produced using inventories reflecting single landslide-triggering events,i.e.,earthquakes and heavy rain storms.Two groups of landslides were utilized: one group containing all landslides triggered by extreme rainfall events between 1995 and 2003 and the other group containing slope failures caused by the 2008 Wenchuan earthquake.Subsequently,the individual maps were combined to illustrate the locations of maximum landslide probability.The use of the resulting three landslide susceptibility maps for landslide forecasting,spatial planning and for developing emergency response actions are discussed.The combined susceptibility map illustrates the total landslide susceptibility in the study area.     

GIS-based earthquake-triggered landslide hazard zoning using contributing weight model

Earthquake-triggered landslides have aroused widespread attention because of their tremendous ability to harm people's lives and properties.The best way to avoid and mitigate their damage is to develop landslide hazard maps and make them available to the public in advance of an earthquake.Future construction can then be built according to the level of hazard and existing structures can be retrofit as necessary.During recent years various approaches have been made to develop landslide hazard maps using statistical analysis or physical models.However,these methods have limitations.This study introduces a new GIS-based approach,using the contributing weight model,to evaluate the hazard of seismically-induced landslides.In this study,the city and surrounding area of Dujiangyan was selected as the research area because of its moderate-high seismic activity.The parameters incorporated into the model that related to the probability of landslide occurrence were:slope gradient,slope aspect,geomorphology,lithology,base level,surface roughness,earthquake intensity,fault proximity,drainage proximity,and road proximity.The parameters were converted into raster data format with a resolution of 25×25m2 pixels.Analysis of the GIS correlations shows that the highest earthquake-induced landslide hazard areas are mainly in the hills and in some of the moderately steep mountainous areas of central Dujiangyan.The highest hazard zone covers an area of 11.1% of the study area,and the density distribution of seismically-induced landslides was 3.025/km2 from the 2008 Wenchuan earthquake.The moderately hazardous areas are mainly distributed within the moderately steep mountainous regions of the northern and southeastern parts of the study area and the hills of the northeastern part;covering 32.0% of the study area and with a density distribution of 2.123/km2 resulting from the Wenchuan earthquake.The lowest hazard areas are mainly distributed in the topographically flat plain in the northeastern part and some of the relatively gently slopes in the moderately steep mountainous areas of the northern part of Dujiangyan and the surrounding area.The lowest hazard areas cover 56.9% of the study area and exhibited landslide densities of 0.941/km2 and less from the Wenchuan earthquake.The quality of the hazard map was validated using a comparison with the distribution of landslides that were cataloged as occurring from the Wenchuan earthquake.43.1% of the study area consists of high and moderate hazardous zones,and these regions include 83.5% of landslides caused by the Wenchuan earthquake.The successful analysis shows that the contributing weight model can be effective for earthquake-triggered landslide hazard appraisal.The model's results can provide the basis for risk management and regional planning is.     

GIS-based landslide susceptibility mapping using numerical risk factor bivariate model and its ensemble with linear multivariate regression and boosted regression tree algorithms

In this study, a novel approach of the landslide numerical risk factor(LNRF) bivariate model was used in ensemble with linear multivariate regression(LMR) and boosted regression tree(BRT) models, coupled with radar remote sensing data and geographic information system(GIS), for landslide susceptibility mapping(LSM) in the Gorganroud watershed, Iran. Fifteen topographic, hydrological, geological and environmental conditioning factors and a landslide inventory(70%, or 298 landslides) were used in mapping. Phased array-type L-band synthetic aperture radar data were used to extract topographic parameters. Coefficients of tolerance and variance inflation factor were used to determine the coherence among conditioning factors. Data for the landslide inventory map were obtained from various resources, such as Iranian Landslide Working Party(ILWP), Forestry, Rangeland and Watershed Organisation(FRWO), extensive field surveys, interpretation of aerial photos and satellite images, and radar data. Of the total data, 30% were used to validate LSMs, using area under the curve(AUC), frequency ratio(FR) and seed cell area index(SCAI).Normalised difference vegetation index, land use/land cover and slope degree in BRT model elevation, rainfall and distance from stream were found to be important factors and were given the highest weightage in modelling. Validation results using AUC showed that the ensemble LNRF-BRT and LNRFLMR models(AUC = 0.912(91.2%) and 0.907(90.7%), respectively) had high predictive accuracy than the LNRF model alone(AUC = 0.855(85.5%)). The FR and SCAI analyses showed that all models divided the parameter classes with high precision. Overall, our novel approach of combining multivariate and machine learning methods with bivariate models, radar remote sensing data and GIS proved to be a powerful tool for landslide susceptibility mapping.     

Optimization of causative factors using logistic regression and artificial neural network models for landslide susceptibility assessment in Ujung Loe Watershed,South Sulawesi Indonesia

Landslide susceptibility maps(LSMs) play a vital role in assisting land use planning and risk mitigation. This study aims to optimize causative factors using logistic regression(LR) and an artificial neural network(ANN) to produce a LSM. The LSM is produced with 11 causative factors and then optimized using forward-stepwise LR(FSLR), ANN, and their combination(FSLR-ANN) until eight causative factors were found for each method. The ANN method produced superior validation results compared with LR. The ROC values for the training data set ranges between 0.8 and 0.9. On the other hand, validation with the percentage of landslide fall into LSM class high and very high, ANN method was higher(92.59%) than LR(82.12%). FSLR-ANN with nine causative factors gave the best validation results with respect to area under curve(AUC) values, and validation with the percentage of landslide fall into LSM class high and very high. In conclusion, ANN was found to be better than LR when producing LSMs. The best Optimization was combination of FSLR-ANN with nine causative factors and AUC success rate 0.847, predictive rate 0.844 and validation with landslide fall into high and very high class with 91.30%. It is an encouraging preliminary model towards a systematic introduction of FSLR-ANN model for optimization causative factors in landslide susceptibility assessment in the mountainous area of Ujung Loe Watershed.     

水库滑坡涌浪风险研究现状和灾害链风险管控实践

滑坡作为水库库区主要地质灾害类型之一,其风险研究一直是近年来的研究热点。水库滑坡涌浪的产生使滑坡灾害的影响范围由滑坡源本身扩散到上下游数千米,极大地扩大了滑坡风险的承灾体类型与数量以及灾害损失程度。因涉及交叉学科领域,滑坡涌浪风险评估是滑坡风险灾害链评价的难点与前沿课题。本文综合了前人近几十年来的研究成果,首先从危险性、易损性以及风险3个方面出发,对国内外的滑坡涌浪风险研究现状和常用的研究方法进行了概述,并对重点代表性研究成果进行了述评分析,针对滑坡涌浪风险研究方面的新进展进行了介绍,包括考虑实际河道地形复杂性的试验研究、聚焦于滑坡-水体相互作用机制的多种数值模拟方法耦合研究,以及基于多种承灾体类型的易损性评价体系等。然后对近年来三峡库区发生过的多起滑坡涌浪风险管控实例的过程与后果进行了详细的阐述。最后基于多年的研究经验提出了滑坡涌浪灾害链风险研究的新方向和新思路,即涌浪风险应与滑坡风险评价体系相互融合,并沿着定量化、规范化、精细化的方向发展。      

Statistical landslide susceptibility assessment in a dynamic environment: A case study for Lanzhou City,Gansu Province,NW China

This study presents a statistical landslide susceptibility assessment (LSA) in a dynamic environment. The study area is located in the eastern part of Lanzhou, NW China. The Lanzhou area has exhibited rapid urbanization rates over the past decade associated with greening, continuous land use change, and geomorphic reshaping activities. To consider the dynamics of the environment in the LSA, multitemporal data for landslide inventories and the corresponding causal factors were collected. The weights of evidence (WofE) method was used to perform the LSA. Three time stamps, i.e., 2000, 2012, and 2016, were selected to assess the state of landslide susceptibility over time. The results show a clear evolution of the landslide susceptibility patterns that was mainly governed by anthropogenic activities directed toward generating safer building grounds for civil infrastructure. The low and very low susceptibility areas increased by approximately 10% between 2000 and 2016. At the same time, areas of medium, high and very high susceptibility zones decreased proportionally. Based on the results, an approach to design the statistical LSA under dynamic conditions is proposed, the issues and limitations of this approach are also discussed. The study shows that under dynamic conditions, the requirements for data quantity and quality increase significantly. A dynamic environment requires greater effort to estimate the causal relations between the landslides and controlling factors as well as for model validation.     

Rapid susceptibility mapping of co-seismic landslides triggered by the 2013 Lushan Earthquake using the regression model developed for the 2008 Wenchuan Earthquake

The primary objective of landslide susceptibility mapping is the prediction of potential landslides in landslide-prone areas.The predictive power of a landslide susceptibility mapping model could be tested in an adjacent area of similar geoenvironmental conditions to find out the reliability.Both the 2008 Wenchuan Earthquake and the 2013 Lushan Earthquake occurred in the Longmen Mountain seismic zone,with similar topographical and geological conditions.The two earthquakes are both featured by thrust fault and similar seismic mechanism.This paper adopted the susceptibility mapping model of co-seismic landslides triggered by Wenchuan earthquake to predict the spatial distribution of landslides induced by Lushan earthquake.Six influencing parameters were taken into consideration: distance from the seismic fault,slope gradient,lithology,distance from drainage,elevation and Peak Ground Acceleration(PGA).The preliminary results suggested that the zones with high susceptibility of coseismic landslides were mainly distributed in the mountainous areas of Lushan,Baoxing and Tianquan counties.The co-seismic landslide susceptibility map was completed in two days after the quake and sent to the field investigators to provide guidance for rescue and relief work.The predictive power of the susceptibility map was validated by ROC curve analysis method using 2037 co-seismic landslides in the epicenter area.The AUC value of 0.710 indicated that the susceptibility model derived from Wenchuan Earthquake landslides showed good accuracy in predicting the landslides triggered by Lushan earthquake.