The Eaux-Bonnes landslide (Western Pyrenees,France): overview of possible triggering factors with emphasis on the role of groundwater

The purpose of this study is to achieve an understanding of the failure mechanisms which caused the Eaux-Bonnes landslide. The geological investigations carried out on the slope of the landslide showed that the sliding mass was cut by numerous faults. The factors controlling the landslide failure were complex, and it is known that neither earthquakes nor heavy precipitation could have triggered the disruption. The groundwater within the solid rock mass has been surveyed, because significant precipitation events during the 2 years preceding the beginning of the paroxysmal phase of the landslide could have led to an increase in pore water pressure along these fractures, thereby triggering the landslide. In order to achieve a full understanding of the failure mechanism, and to identify the origin of the groundwater, a hydrogeochemical survey was carried out over a period of 1 year. The results reveal the existence of high sulphate concentrations in the groundwater originating in springs located at the bottom of the landslide. The sulphate concentrations are correlated with high calcium concentrations, and clearly indicate the presence of gypsum in the vicinity of the lower reaches of the landslide. The presence of gypsum in this area of the Pyrenees suggests that deep groundwater played a role in triggering the landslide.     

滑坡易发性危险性风险评价例析

从易发性、危险性、风险的概念入手,依据国际上流行和通用的滑坡风险评价与管理理论,分析了易发性评价的内容,包括易发性评价到危险性评价需要增加的评价要素,以及从危险性评价到风险评价需要增加的评价要素,阐明了这三种评价之间的联系和区别。并通过延安宝塔区的滑坡易发性、危险性和风险的评价与区划具体说明三者的做法和结果。     

Application of logistic regression for landslide susceptibility zoning of Cekmece Area, Istanbul, Turkey

As a result of industrialization, throughout the world, cities have been growing rapidly for the last century. One typical example of these growing cities is Istanbul, the population of which is over 10 million. Due to rapid urbanization, new areas suitable for settlement and engineering structures are necessary. The Cekmece area located west of the Istanbul metropolitan area is studied, because the landslide activity is extensive in this area. The purpose of this study is to develop a model that can be used to characterize landslide susceptibility in map form using logistic regression analysis of an extensive landslide database. A database of landslide activity was constructed using both aerial-photography and field studies. About 19.2% of the selected study area is covered by deep-seated landslides. The landslides that occur in the area are primarily located in sandstones with interbedded permeable and impermeable layers such as claystone, siltstone and mudstone. About 31.95% of the total landslide area is located at this unit. To apply logistic regression analyses, a data matrix including 37 variables was constructed. The variables used in the forwards stepwise analyses are different measures of slope, aspect, elevation, stream power index (SPI), plan curvature, profile curvature, geology, geomorphology and relative permeability of lithological units. A total of 25 variables were identified as exerting strong influence on landslide occurrence, and included by the logistic regression equation. Wald statistics values indicate that lithology, SPI and slope are more important than the other parameters in the equation. Beta coefficients of the 25 variables included the logistic regression equation provide a model for landslide susceptibility in the Cekmece area. This model is used to generate a landslide susceptibility map that correctly classified 83.8% of the landslide-prone areas.     

A statistical approach to landslide risk modelling at basin scale: from landslide susceptibility to quantitative risk assessment

A procedure for landslide risk assessment is presented. The underlying hypothesis is that statistical relationships between past landslide occurrences and conditioning variables can be used to develop landslide susceptibility, hazard and risk models. The latter require also data on past damages. Landslides occurred during the last 50 years and subsequent damages were analysed. Landslide susceptibility models were obtained by means of Spatial Data Analysis techniques and independently validated. Scenarios defined on the basis of past landslide frequency and magnitude were used to transform susceptibility into quantitative hazard models. To assess vulnerability, a detailed inventory of exposed elements (infrastructures, buildings, land resources) was carried out. Vulnerability values (0–1) were obtained by comparing damages experienced in the past by each type of element with its actual value. Quantitative risk models, with a monetary meaning, were obtained for each element by integrating landslide hazard and vulnerability models. Landslide risk models showing the expected losses for the next 50 years were thus obtained for the different scenarios. Risk values obtained are not precise predictions of future losses but rather a means to identify areas where damages are likely to be greater and require priority for mitigation actions.     

Data driven bivariate landslide susceptibility assessment using geographical information systems: a method and application to Asarsuyu catchment, Turkey

In the last decades, landslide hazard assessment has attracted many researchers' attention. A number of parameters are suggested to be responsible to quantitatively explain the mechanism of landslides; many of these parameters are very important and factual. However, some data types and models are site-specific and could not be applied to different locations. Furthermore, the data stored in continuous parameter maps are divided into a number of classes arbitrarily, depending on the vision of the expert. Basically, this division controls the result of bivariate analysis. Besides, the responsible portion of the parameter map controlling the mechanism is also weighted arbitrarily. Based on these two facts, the class boundaries put a prejudice on the produced susceptibility/hazard maps, which result in dependence on the knowledge of the user rather than being dependent on the data and the fact itself. The aim of this study is to refine the previously defined methods in a more data-dependent trend. To achieve this goal, two new concepts: seed cells and percentile maps are introduced. Seed cells are the zones that are considered to represent the best undisturbed morphological decision rules (conditions before landslide occurs) and would be achieved by adding a buffer zone to the crown and flank areas of the landslide. To quantitatively classify the input parameter maps, the data distributions of seed cells in the parameter maps are divided into a number of classes on the basis of their distribution's percentile break-points upon which the parameter maps are directly dependent on the seed cell distributions, hence to the data itself.     

Uncertainty pattern in landslide susceptibility prediction modelling: Effects of different landslide boundaries and spatial shape expressions

In some studies on landslide susceptibility mapping (LSM), landslide boundary and spatial shape characteristics have been expressed in the form of points or circles in the landslide inventory instead of the accurate polygon form. Different expressions of landslide boundaries and spatial shapes may lead to substantial differences in the distribution of predicted landslide susceptibility indexes (LSIs); moreover, the presence of irregular landslide boundaries and spatial shapes introduces uncertainties into the LSM. To address this issue by accurately drawing polygonal boundaries based on LSM, the uncertainty patterns of LSM modelling under two different landslide boundaries and spatial shapes, such as landslide points and circles, are compared. Within the research area of Ruijin City in China, a total of 370 landslides with accurate boundary information are obtained, and 10 environmental factors, such as slope and lithology, are selected. Then, correlation analyses between the landslide boundary shapes and selected environmental factors are performed via the frequency ratio (FR) method. Next, a support vector machine (SVM) and random forest (RF) based on landslide points, circles and accurate landslide polygons are constructed as point-, circle- and polygon-based SVM and RF models, respectively, to address LSM. Finally, the prediction capabilities of the above models are compared by computing their statistical accuracy using receiver operating characteristic analysis, and the uncertainties of the predicted LSIs under the above models are discussed. The results show that using polygonal surfaces with a higher reliability and accuracy to express the landslide boundary and spatial shape can provide a markedly improved LSM accuracy, compared to those based on the points and circles. Moreover, a higher degree of uncertainty of LSM modelling is present in the expression of points because there are too few grid units acting as model input variables. Additionally, the expression of the landslide boundary as circles introduces errors in measurement and is not as accurate as the polygonal boundary in most LSM modelling cases. In addition, the results under different conditions show that the polygon-based models have a higher LSM accuracy, with lower mean values and larger standard deviations compared with the point- and circle-based models. Finally, the overall LSM accuracy of the RF is superior to that of the SVM, and similar patterns of landslide boundary and spatial shape affecting the LSM modelling are reflected in the SVM and RF models.     

Spatial landslide susceptibility assessment using machine learning techniques assisted by additional data created with generative adversarial networks

In recent years,landslide susceptibility mapping has substantially improved with advances in machine learning.However,there are still challenges remain in landslide mapping due to the availability of limited inventory data.In this paper,a novel method that improves the performance of machine learning techniques is presented.The proposed method creates synthetic inventory data using Generative Adversarial Networks(GANs)for improving the prediction of landslides.In this research,landslide inventory data of 156 landslide locations were identified in Cameron Highlands,Malaysia,taken from previous projects the authors worked on.Elevation,slope,aspect,plan curvature,profile curvature,total curvature,lithology,land use and land cover(LULC),distance to the road,distance to the river,stream power index(SPI),sediment transport index(STI),terrain roughness index(TRI),topographic wetness index(TWI)and vegetation density are geo-environmental factors considered in this study based on suggestions from previous works on Cameron Highlands.To show the capability of GANs in improving landslide prediction models,this study tests the proposed GAN model with benchmark models namely Artificial Neural Network(ANN),Support Vector Machine(SVM),Decision Trees(DT),Random Forest(RF)and Bagging ensemble models with ANN and SVM models.These models were validated using the area under the receiver operating characteristic curve(AUROC).The DT,RF,SVM,ANN and Bagging ensemble could achieve the AUROC values of(0.90,0.94,0.86,0.69 and 0.82)for the training;and the AUROC of(0.76,0.81,0.85,0.72 and 0.75)for the test,subsequently.When using additional samples,the same models achieved the AUROC values of(0.92,0.94,0.88,0.75 and 0.84)for the training and(0.78,0.82,0.82,0.78 and 0.80)for the test,respectively.Using the additional samples improved the test accuracy of all the models except SVM.As a result,in data-scarce environments,this research showed that utilizing GANs to generate supplementary samples is promising because it can improve the predictive capability of common landslide prediction models.     

Earthquake-induced landslide hazard and vegetation recovery assessment using remotely sensed data and a neural network-based classifier: a case study in central Taiwan

A catastrophic earthquake with a Richter magnitude of 7.3 occurred in the Chi-Chi area of Nantou County on 21 September 1999. Large-scale landslides were generated in the Chiufenershan area of Nantou County in central Taiwan. This study used a neural network-based classifier and the proposed NDVI-based quantitative index coupled with multitemporal SPOT images and digital elevation models (DEMs) for the assessment of long-term landscape changes and vegetation recovery conditions at the sites of these landslides. The analyzed results indicate that high accuracy of landslide mapping can be extracted using a neural network-based classifier, and the areas affected by these landslides have gradually been restored from 211.52 ha on 27 September 1999 to 113.71 ha on 11 March 2006, a reduction of 46.24%, after six and a half years of assessment. In accordance with topographic analysis at the sites of the landslides, the collapsed and deposited areas of the landslide were 100.54 and 110.98 ha, with corresponding debris volumes of 31,983,800 and 39,339,500 m³. Under natural vegetation succession, average vegetation recovery rate at the sites of the landslides reached 36.68% on 11 March 2006. The vegetation recovery conditions at the collapsed area (29.17%) are shown to be worse than at the deposited area (57.13%) due to topsoil removal and the steep slope, which can be verified based on the field survey. From 1999 to 2006, even though the landslide areas frequently suffered from the interference of typhoon strikes, the vegetation succession process at the sites of the landslides was still ongoing, which indicates that nature, itself, has the capability for strong vegetation recovery for the denudation sites. The analyzed results provide very useful information for decision-making and policy-planning in the landslide area.     

Parametrical landslide modeling for the hydrogeological susceptibility assessment: from the Crati Valley to the Cavallerizzo landslide (Southern Italy)

The article deals with a tool for landslides susceptibility assessment as a function of the hydrogeological setting at different scales. The study has been applied to a test area located in Southern Italy. First, a 3D groundwater flow model was implemented for a large-scale area. The simulation of several groundwater conditions compared with the landslide activity map allows drawing a hydrogeological susceptibility map. Then, a slope scale analysis was carried out for the Cavallerizzo landslide. For this purpose, a 2D groundwater parametrical modeling was coupled with a slope stability analysis; the simulation was carried out by changing the values of the main hydrogeological parameters (recharge, groundwater supply level, etc.). The results enabled to connect the slope instability to some hydrogeological characteristics that are easy to survey and to monitor (e.g., rainfall, piezometrical level, and spring discharge), pointing out the hazard thresholds with regards to different triggering phenomena.     

Regional bias of landslide data in generating susceptibility maps using logistic regression: Case of Hong Kong Island

On the basis of 1,834 landslide data for Hong Kong Island (HKI), landslide susceptibility maps were generated using logistic regression and GIS. Regional bias of the landslide inventory is examined by dividing the whole HKI into a southern and a northern region, separated by an east-west trending water divide. It was found that the susceptibility map of southern HKI generated by using the southern data differs significantly from that generated by using northern data, and similar conclusion can be drawn for the northern HKI. Therefore, a susceptibility map of HKI was established based on regional data analysis, and it was found to reflect closely the spatial distributions of historical landslides. Elevation appears to be the most dominant factor in controlling landslide occurrence, and this probably reflects that human developments are concentrated at certain elevations on the island. Classification plot, goodness of fit, and occurrence ratio were used to examine the reliability of the proposed susceptibility map. The size of landslide susceptible zones varies depending on the data sets used, thus this demonstrates that the historical landslide data may be biased and affected by human activities and geological settings on a regional basis. Therefore, indiscriminate use of regional-biased data should be avoided.     

承灾体易损性评估模型与滑坡灾害风险度指标

承灾体易损性定量评估是制约滑坡灾害风险评估研究的瓶颈问题。为此,以滑坡体冲击冲量为致灾强度指标、建筑物整体抗剪力为抗灾性能指标,推导出典型承灾体易损性定量评估模型。在此基础上,考虑滑体运动特征参数随机性对易损性的影响,提出风险曲线和最大风险度指标的概念,以反映滑坡灾害成灾全过程中不确定性对灾害后果的影响。并采用该模型分析了坡体几何特征参数、受灾体空间位置以及受灾体抗灾性能对易损性的影响规律。将风险度指标应用于算例分析,并与以往方法进行了比较,分析发现,建立的易损性定量评估模型可以反映二维简化情况下受灾体毁损程度与各种影响因素之间关系的基本规律,为易损性定量评估提供了一种途径。     

Accuracy assessment of InSAR derived input maps for landslide susceptibility analysis: a case study from the Swiss Alps

In recent years SAR interferometry has become a widely used technique for measuring altitude and displacement of the surface of the earth. Both these capabilities are highly relevant for landslide susceptibility studies. Although there are many problems that make the use of SAR interferometry less suitable for landslide inventory mapping, it’s use in landslide monitoring and in the generation of input maps for landslide susceptibility assessment looks very promising. The present work attempts to evaluate the usefulness and limitations of this technique based on a case study in the Swiss Alps. Input maps were generated from ERS repeat pass data using SAR interferometry. A land cover map has been generated by image classification of multi-temporal SAR intensity images. An InSAR DEM was generated and a number of maps were derived from it, such as slope-, aspect, altitude- and slope form classes. These maps were used to generate landslide and rockfall susceptibility maps, which give fairly well acceptable results. However, a comparison of the InSAR DEM with the conventional Swisstopo DEM, indicated significant errors in the absolute height and slope angles derived from InSAR, especially along the ridges and in the valleys. These errors are caused by low coherence mostly due to layover and shadow effects. Visual comparison of stereo images created from hillshading maps and corresponding DEMs demonstrate that a considerable amount of topographic details have been lost in the InSAR-derived DEM. It is concluded that InSAR derived input maps are not ideal for landslide susceptibility assessment, but could be used if more accurate data is lacking.     

Statistical functions used for spatial modelling due to assessment of landslide distribution and landscape-interaction factors in Iran

Landslides influence the capacity for safe and sustainable development of mountainous environments.This study explores the spatial distribution of and the interactions between landslides that are mapped using global positioning system(GPS) and extensive field surveys in Mazandaran Province,Iran.Point-pattern assessment is undertaken using several univariate summary statistical functions,including pair correlation,spherical-contact distribution,nearest-neighbor analysis,and O-ring analysis,as well as bivariate summary statistics,and a markcorrelation function.The maximum entropy method was applied to prioritize the factors controlling the incidence of landslides and the landslides susceptibility map.The validation processes were considered for separated 30%data applying the ROC curves,fourfold plot,and Cohen's kappa index.The results show that pair correlation and O-ring analyses satisfactorily predicted landslides at scales from 1 to 150 m.At smaller scales,from 150 to 400 m,landslides were randomly distributed.The nearest-neighbor distribution function show that the highest distance to the nearest landslide occurred in the 355 m.The spherical-contact distribution revealed that the patterns were random up to a spatial scale of 80 m.The bivariate correlation functions revealed that landslides were positively linked to several linear features(including faults,roads,and rivers) at all spatial scales.The mark-correlation function showed that aggregated fields of landslides were positively correlated with measures of land use,lithology,drainage density,plan curvature,and aspect,when the numbers of landslides in the groups were greater than the overall average aggregation.The results of analysis of factor importance have showed that elevation(topography map scale:1:25,000),distance to roads,and distance to rivers are the most important factors in the occurrence of landslides.The susceptibility model of landslides indicates an excellent accuracy,i.e.,the AUC value of landslides was 0.860.The susceptibility map of landslides analyzed has shown that 35% of the area is low susceptible to landslides.     

A spatial decision support system for extracting the core factors and thresholds for landslide susceptibility map

Spatial decision support system (SDSS) is an interactive, computer-based system designed to support a user in achieving a higher effectiveness of decision-making while solving a semi-structured spatial data. Satellite Remote Sensing and Digital Elevation Modeling are providing a systematic, rational framework for advancing scientific knowledge of our SDSS of geophysical phenomena that, often lead to observe the natural hazards or resources. Taking the advantage of these, more specifically, our study focused on using these to collect and measure the landslide data on a vast area located at Shei Pa National Park, Miao Li, Taiwan. Our source data includes (1) Digital Elevation Modeling is also used to investigate the landform, and (2) remote sensing image data are also employed to analyze the vegetation conditions. In addition, the process of generating landslide susceptibility maps involved on how to effectively extract the site-condition dominant attributes and thresholds for displaying the landslide occurrence accurately. Thus, the information from landslide must be categorized and thoroughly evaluated by an Advanced Data Mining Technique — Entropy-based classification method to construct the landslide knowledge rules. The knowledge scope with regards to core factors and thresholds are solved. Then, the susceptibility hazard maps are drawn and verifications are made. On the other hand, the conventional statistical method of Logistic Regression is used for comparison.     

Geomorphology and landslide susceptibility assessment using GIS and bivariate statistics: a case study in southern Italy

In this article, the results of a study aimed to assess the landslide susceptibility in the Calaggio Torrent basin (Campanian Apennines, southern Italy) are presented. The landslide susceptibility has been assessed using two bivariate-statistics-based methods in a GIS environment. In the first method, widely used in the existing literature, weighting values (Wi) have been calculated for each class of the selected causal factors (lithology, land-use, slope angle and aspect) taking into account the landslide density (detachment zones + landslide body) within each class. In the second method, which is a modification of the first method, only the landslide detachment zone (LDZ) density has been taken into account to calculate the weighting values. This latter method is probably characterized by a major geomorphological coherence. In fact, differently from the landslide bodies, LDZ must necessarily occur in geoenvironmental classes prone to failure. Thus, the calculated Wi seem to be more reliable in estimating the propensity of a given class to generate failure. The thematic maps have been reclassified on the basis of the calculated Wi and then overlaid, with the purpose to produce landslide susceptibility maps. The used methods converge both in indicating that most part of the study area is characterized by a high–very high landslide susceptibility and in the location and extent of the low-susceptible areas. However, an increase of both the high–very high and moderate–high susceptible areas occurs in using the second method. Both the produced susceptibility maps have been compared with the geomorphological map, highlighting an excellent coherence which is higher using method-2. In both methods, the percentage of each susceptibility class affected by landslides increases with the degree of susceptibility, as expected. However, the percentage at issue in the lowest susceptibility class obtained using method-2, even if low, is higher than that obtained using method-1. This suggests that method-2, notwithstanding its major geomorphological coherence, probably still needs further refinements.     

Landslide susceptibility and hazard mapping in Australia for land-use planning — with reference to challenges in metropolitan suburbia

This paper provides an overview of the history and current status of landslide susceptibility and hazard mapping for land-use zoning in Australia. It also describes a case study of landslide hazard mapping in a medium density, coastal, suburban residential area of metropolitan Sydney, New South Wales, Australia, with relatively steep terrain. Issues covered include identification and mapping of existing and potential landslides, and susceptibility and hazard zoning for regulatory management and land-use planning. The method involves application of the principles contained within the AGS (2000) guideline, and as updated by the AGS (2007 a,b,c,d,e) suite of guidelines.     

Analysis of landslide susceptibility in the Suusamyr region, Tien Shan: statistical and geotechnical approach

The Suusamyr region is located in the northern part of the Tien Shan Range in Central Asia. In 1992, this region was hit by the Ms = 7.3 Suusamyr earthquake triggering several large landslides along the Suusamyr Valley and on the southern slopes of the adjacent Suusamyr Range. One of these landslides had been investigated by geophysical and geotechnical methods in order to determine local trigger factors. The present paper focuses on the influence of geological and morphological factors upon landslide occurrence on a regional scale. The analysis is based on a digital data set including landslides triggered in 1992 and several older landslides as well as various types of digital elevation models (DEMs), ASTER image data, and geological and active fault maps. These data were combined to compute landslide susceptibility (LS) maps using statistical methods, Landslide Factor and Conditional Analyses (LFA, CA), as well as a geotechnical one, the Newmark's Method (NM). The landslide data set was also analyzed with respect to the size–frequency relationship. An erratum to this article can be found at     

Failure and flow development of a collapse induced complex landslide: the 2005 Kuzulu (Koyulhisar, Turkey) landslide hazard

Koyulhisar located in a slope of hilly region and constructed in the side of a mountain along the North Anatolian Fault Zone is frequently subject to landslides. A catastrophic landslide occurred on the morning of 17 March 2005 in the North of the Kuzulu district of Koyulhisar (Sivas, Turkey). This landslide caused widespread loss of life, and damage to buildings, and lifelines. Fifteen people were dead and five were injured, 21 houses and a minaret were covered and damaged severely. The case study presented in this paper describes and analyses the results of the detailed surveys of an interesting landslide in Kuzulu district of Koyulhisar (Sivas, Turkey), based on field and laboratory measurements and monitoring of the slide area. Landslide initiated as a collapse, and developed into debris avalanches in the valley. This phenomenon caused a disaster in the Kuzulu district. The importance of this landslide in particular has been recognized both in terms of its consequence for the people and structures and in terms of its role in allowing an understanding of process and properties of landslide triggered by a collapse in limestone karst. In view of the potential for such events to occur again in this area and environs, understanding of the failure mechanism is very crucial.