Spatial data analysis and application of evidential belief functions to shallow landslide susceptibility mapping at Mt. Umyeon,Seoul, Korea

Predictive mapping of landslide occurrence at the regional scale was performed at Mt. Umyeon, in the southern part of Seoul, Korea, using an evidential belief function (EBF) model. To generate the landslide susceptibility map, approximately 90 % of 163 actual landslide locations were randomly selected as a training set, and about 10 % of them were used as a validation set. Spatial data sets relevant to landslide occurrence (topographic factors, hydrologic factors, forest factors, soil factors, and geologic factors) were analyzed in a geographic information system environment. In this study, landslide susceptibility was assessed on the basis of mass function assignment (belief, disbelief, uncertainty, and plausibility) and integration within a data-driven approach. The most representative of the resulting integrated susceptibility maps (the belief map) was validated using the receiver operating characteristic (ROC) method. The verification result showed that the model had an accuracy of 74.3 % and a predictive accuracy of 88.1 %. The frequency ratio (FR) model was also used for comparison with the EBF model. Prediction and success rates of 72.1 and 85.9 % were achieved using the FR model. The validation results showed satisfactory agreement between the susceptibility map and the existing landslide location data. The EBF model was more accurate than the FR model for landslide prediction in the study area. The results of this study can be used to mitigate landslide-induced hazards and for land-use planning.     

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.

     

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.     

A statistical and geographical information system analysis for groundwater intrinsic vulnerability: a validated case study from Sfax–Agareb,Tunisia

The groundwater vulnerability to the pollution assessment was considered as an efficient tool to limit and to control its quantitative and qualitative degradation risks. The DRASTIC high, moderate and low groundwater vulnerability zones of the Sfax–Agareb basin (Tunisia) cover about 10, 29 and 61% of the study area, respectively. The validation of the DRASTIC vulnerability map was undertaken through comparison of areas of high nitrate concentration and their relative vulnerability index. The DRASTIC vulnerability map illustrates a good rate of coincidence between the nitrate concentration ranges and the various vulnerability classes as recognized by statistical analysis. The reliability of the final vulnerability map has been tested, showing a general positive trend relating the mean nitrate concentration in the wells to their relative vulnerability classes (R²=0.88). When correlating the 214 available groundwater nitrate concentrations to the DRASTIC index in these wells location, a significant positive correlation with Cor=0.55 was found.     

基于ArcGIS的四川松新黑水河地区滑坡危险性评价

以松新黑水河地区作为研究区域,基于遥感解译、野外调查统计、地质环境分析、典型滑坡研究的基础上,选取坡度、工程地质岩组、斜坡结构、断裂构造、降雨、人类工程活动等6个与滑坡发生相关的要素作为危险性评价因子。在ArcGIS空间分析模块中,采用自然断点法的数据分类方法,运用频率比——面域模型,对研究区滑坡危险性进行了评价与区划。研究结果表明:松新黑水河地区滑坡危险性分区为:高危险区、中等危险区、低危险区3个区域,所占研究区面积比例分别为32%、50%、18%。     

Landslide-susceptibility mapping in a semi-arid mountain environment: an example from the southern slopes of Sierra Nevada (Granada,Spain)

Landslide susceptibility is analysed in a semi-arid mountain environment, on the southern slope of Sierra Nevada. In a study area of 460 km², 252 landslides were inventoried, affecting 3.2% of the total surface area. These landslides were mainly slides and flows on phyllite, schist and marble units in the Inner Zone of the Betic Cordillera. The most relevant determining factors proved to be elevation, slope angle, slope aspect and lithology. Triggering factors include mainly short-term landslide generation during heavy rainfall, as well as sporadic earthquakes or long-term activation by land-use changes, river over-excavation, etc. Although landslide susceptibility, assessed by the GIS matrix method, is predominantly low, some 15% of the study area shows moderate to very high susceptibility, coinciding with the sites of public works in the region. The map drawn was validated by the degree-of-fit method, registering values above 83.2% for the zones of high and very high susceptibility.     

A method for quick assessment of earthquake-triggered landslide hazards: a case study of the Mw6.1 2014 Ludian,China earthquake

Rapid assessment of the distribution of earthquake-triggered landslides is an important component of effective disaster mitigation. The effort should be based on both seismic landslide susceptibility and the ground shaking intensity, which is usually measured by peak ground acceleration (PGA). In this paper, we address this issue by analyzing data from the Mw6.1 2014 Ludian, China earthquake. The Newmark method of rigid-block modeling was applied to calculate the critical acceleration of slopes in the study area, which serve as measurement of slope stability under seismic load. The assessment of earthquake-triggered landslide hazard was conducted by comparing these critical accelerations with the distribution of known PGA values. The study area was classified into zones of five levels of landslide hazard: high, moderate high, moderate, light, and very light. Comparison shows that the resulting landslide hazard zones agree with the actual distribution of earthquake-triggered landslides. Nearly 70% of landslides are located in areas of high and moderately high hazard, which occupy only 17% of the study region. This paper demonstrates that using PGA, combined with the analysis of seismic landslide susceptibility, allows a reliable assessment of earthquake-triggered landslides hazards. This easy-operation mapping method is expected to be helpful in emergency preparedness planning, as well as in seismic landslide hazard zoning.

     

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.     

四川茂县叠溪镇新磨村滑坡特征与成因机制初步研究

2017年6月24日6时许,四川省茂县叠溪镇新磨村新村组后山约450×104 m3的山体发生顺层高位滑动,导致10人死亡和73人失踪,引起国内外的广泛关注。在对灾害现场进行大量地质调查的基础上,综合运用卫星遥感、无人机航拍、地面合成孔径雷达监测等技术手段,初步揭示滑坡的运动过程和成因机制,并对滑坡周边直接受主滑坡动力作用而产生的欠稳定岩土体特征和危险性进行分析评价。初步结果认为,滑坡源区山体在1933年叠溪地震中被震裂产生拉张裂缝,之后在多次地震、长期重力以及降雨作用下,最终整体失稳破坏。滑坡从小规模垮塌到主滑体启动、失稳运动、到最终停积整个过程用时仅120 s,其中主滑坡仅60 s,运动距离约2.6 km,最大运动速度达74.6 m/s,属于典型高速远程滑坡–碎屑流。新磨村滑坡具有高位、高度隐蔽性等突出特点,仅靠传统的调查排查和群测群防手段,已很难对灾害隐患进行早期识别和提前发现,必须尽快推广应用现代高精度对地观测技术(如In SAR,Li DAR,高清无人机航拍等),对我国西南山区类似隐蔽性高位崩滑灾害隐患进行排查和主动防范。     

Coping with Global Environmental Change,Disasters and Security: Threats,Challenges, Vulnerabilities and Risks

Since the publication of the third assessment report of The Intergovernmental Panel on Climate Change, vulnerability to climate change has become an important research question. Vulnerability assessment on the urban scale has become a major issue. This paper describes a conceptual framework for modelling vulnerability at the urban scale, the Climate Change Vulnerability Assessment model. The model is applied to Shanghai, a typical geographically vulnerable and rapidly-urbanizing case study area. Using Arc-GIS, a vulnerability map was created for understanding the spatial dynamics of climate change vulnerability in Shanghai. An additional process, combined with the weighting coefficients, produced different vulnerable areas. Based on the vulnerability map, we located several high risk areas. The vulnerability of each area was assessed. Identifying the risks in each case and associating them with a specific region can be useful for decision makers.     

Evaluating landslide susceptibility based on cluster analysis,probabilistic methods,and artificial neural networks

In this study, the cluster analysis (CA), probabilistic methods, and artificial neural networks (ANNs) are used to predict landslide susceptibility. The Geographic Information System (GIS) is used as the basic tool for spatial data management. CA is applied to select non-landslide dataset for later analysis. A probabilistic method is suggested to calculate the rating of the relative importance of each class belonging to each conditional factor. ANN is applied to calculate the weight (i.e., relative importance) of each factor. Using the ratings and the weights, it is proposed to calculate the landslide susceptibility index (LSI) for each pixel in the study area. The obtained LSI values can then be used to construct the landslide susceptibility map. The aforementioned proposed method was applied to the Longfeng town, a landslide-prone area in Hubei province, China. The following eight conditional factors were selected: lithology, slope angle, distance to stream/reservoir, distance to road, stream power index (SPI), altitude, curvature, and slope aspect. To assess the conditional factor effects, the weights were calculated for four cases, using 8 factors, 6 factors, 5 factors, and 4 factors, respectively. Then, the results of the landslide susceptibility analysis for these four cases, with and without weighting, were obtained. To validate the process, the receiver operating characteristics (ROC) curve and the area under the curve (AUC) were applied. In addition, the results were compared with the existing landslide locations. The validation results showed good agreement between the existing landslides and the computed susceptibility maps. The results with weighting were found to be better than that without weighting. The best accuracy was obtained for the case with 5 conditional factors with weighting.

     

Landslide susceptibility of the Xiangjiaba Reservoir area associated with the Yaziba Fault

Landslides along active fault zones are important hazards during and after earthquakes. They can also cause secondary disasters such as surges, creation of landslide dams, and flooding, especially in reservoir areas. This study analyzed landslide susceptibility of the Xiangjiaba Reservoir area associated with the Yaziba Fault. Analysis of permanent displacement and failure probabilities were used for the regional assessment. Ground motion attenuation relations were selected and compared to produce a peak acceleration map using the Yaziba Fault as the seismic source. Geotechnical parameters were determined by classification of rock groups and geomorphic data were calculated using GIS tools. A distribution of the permanent displacements and a failure probability map was generated. According to the peak ground acceleration (PGA) map, the C-B model was adopted to present the actual conditions of PGA in greater detail while local specific models are more appropriate if there is little measured data. Results indicated larger displacement values and failures are distributed on both sides of the fault, especially in the hanging wall. The feasibility of the research approach was verified using historic earthquake-induced landslides.     

Erdbebengebiete der Bundesrepublik Deutschland – eine statistische Auswertung

Earthquake regions in Germany – a statistical evaluation. In context with the development of the German Seismic Code DIN 4149: 2005 a new zoning map has been elaborated. Before obtaining the status of an official document, the affected administrative units have to be assigned to the corresponding zones, under the responsibility of the German states. On the basis of recent statistical data provided by the state offices the shares of each federal state to the whole area of the individual seismic zones are evaluated. To gain a more detailed insight concerning the practical consequences, in addition to the size of the covered areas the number of inhabitants and buildings within theses zones are determined and compared. It can be concluded that about 60% of all zone 3 areas belong to Baden‐Wuerttemberg (BW) and about 40% to North Rhine‐Westphalia (NRW), but in contrast and due to the different density of settlement, from the about 0.35 Mio residential buildings in the highest zone3, about 65% of the buildings belong to NRW and only 35% to BW. The number of buildings in the lower zones2 and 1 is – if one follows purely the statistical facts – high. Regarding the generally acknowledged high quality of construction standards in Germany a check of inherent earthquake resistance (of structures without antiseismic provisions) is recommended in order to reduce design requirements. Using the ratio between the reference acceleration of the new and the previous code version and zoning maps regions, different levels of change (ranging from the neglect or reduction to a serious increase of design parameters) are distinguished.     

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.

     

Landslide susceptibility mapping at Ovacık-Karabük (Turkey) using different artificial neural network models: comparison of training algorithms

This study aims to investigate the performances of different training algorithms used for an artificial neural network (ANN) method to produce landslide susceptibility maps. For this purpose, Ovacık region (southeast of Karabük Province), located in the Western Black Sea Region (Turkey), was selected as the study area. A total of 196 landslides were mapped, and a landslide database was prepared. Topographical elevation, slope angle, aspect, wetness index, lithology, and vegetation index parameters were taken into account for the landslide susceptibility analyses. Two different ANN structures, which were composed of single and double hidden layers, were applied to compare the effects of the ANN. Four different training algorithms, namely batch back-propagation, quick propagation, conjugate gradient descent (CGD), and Levenberg–Marquardt, were used for the training stage of the ANN models. Thus, eight different landslide susceptibility maps were produced for the study area using different ANN structures and algorithms. In order to assess the effects and spatial performances of the considered training algorithms on the ANN models, the relative operating characteristics (ROC) and relation value (r_ij) approaches were used. The susceptibility map produced by CGD1 has the highest AUC (0.817) and r_ij values (0.972). Comparison of the susceptibility maps indicated that CGD training algorithm is the slowest one among the other algorithms, but this algorithm showed the highest performance on the results.

     

A case study from Koyulhisar (Sivas-Turkey) for landslide susceptibility mapping by artificial neural networks

A case study for the use of an artificial neural network (ANN) model for landslide susceptibility mapping in Koyulhisar (Sivas-Turkey) is presented. Digital elevation model (DEM) was first constructed using ArcGIS software. Relevant parameter maps were created, including geology, faults, drainage system, topographical elevation, slope angle, slope aspect, topographic wetness index, stream power index, normalized difference vegetation index and distance from roads. Finally, a landslide susceptibility map was constructed using the neural networks. The drawbacks of the method are discussed but as the validation procedures used confirmed the quality of the map produced, it is recommended the use of ANN may be helpful for planners and engineers in the initial assessment of landslide susceptibility.      

Slope instability phenomena in Greece: A statistical analysis

The results of a statistical analysis on landslide phenomena in Greece are presented here. The task is based upon 800 case-histories of landslide incidents, recorded up to 1986, retrieved from an original number of more than 1500 coded engineering reports and studies. The object concerns village areas and the road network. It especially refers to their frequency distribution and their consequences on different geological formations, altitudes, rain-fall, slope angles etc. Furthermore, assessments for the above parameters were made and certain interrelations were studied. Additionally an engineering geological map of Greece as well as landslide distribution and frequency zone maps were compiled on the basis of the evaluated data.     

基于信息量法的重庆云阳县(三峡库区)地质灾害易发性评价

云阳县地处重庆市东部,属三峡库区,构造上位于川东弧形构造带东北段,褶皱形态以宽平的屉形向斜和狭窄的高背斜相间排列,组成隔挡式构造。区内地貌以中-高山峡谷地貌为主,第四纪以来一直处于间歇性抬升状态,地形切割强烈,长江河道从境内穿过,河流水系发育,年降雨量大,地质环境条件脆弱,地质灾害发育。论文基于Arcgis平台,运用信息量法全面分析了影响云阳县地质灾害发育的地形条件、地层岩性、地质构造、河流水系、人类工程活动等因素,建立了云阳县地质灾害易发评价指标体系。评价结果表明,高易发区面积576 km²,占比15.8％,中易发区面积1801.68 km²,占比49.41％；评价结果可为云阳县区域地质灾害防治提供依据,也可为三峡库区地质灾害易发性评价提供参考。     

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.     

Landslide hazard zonation of the Khorshrostam area, Iran

 Landslide hazard zonation is a method to evaluate the risk where there is the potential for landslides. The factors contributing to the hazard in an area can usually be identified, results of the investigations frequently being presented as a landslide hazard zonation map indicating zones of similar risk of the occurrence of a landslide. Korshrostam is one of the areas most susceptible to landslides in Iran with more than 13% of its surface being affected by landslide activity. The effects include damage or disturbance to villages, farmlands and roads as well as the exacerbation of erosion of the land surface and consequently an increase in the rate of sedimentation in the water flowing into the reservoir of the Manjil dam. The method of landslide zonation used in this study was based on a simple grid unit. A number of factors contributing to the likelihood of landsliding were considered, including lithology, slope, tectonic activity, land use and groundwater. For each grid unit, the incidence of landsliding and an assessment of the likely contributory factors were recorded in terms of a surface percentage index (SPI). A computer program was written using fuzzy sets to calculate the hazard potential index (HPI) for each unit. This was used to prepare the landslide hazard zonation map. Received: 10 June 1999 · Accepted: 16 September 1999