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1.
Complete landslide inventories are rarely available. The objectives of this study were to (i) elaborate the influence of incomplete landslide inventories on statistical landslide susceptibility models and to (ii) propose suitable modelling strategies that can reduce the effects of inventory-based incompleteness. In this context, we examined whether the application of a novel statistical approach, namely mixed-effects models, enables predictions that are less influenced by such inventory-based errors.The study was conducted for (i) an area located in eastern Austria and (ii) a synthetically generated data set. The applied methodology consisted of a simulation of two different inventory-based biases and an in-depth evaluation of subsequent modelling results. Inventory-based errors were simulated by gradually removing landslide data within forests and selected municipalities. The resulting differently biased inventories were introduced into logistic regression models while we considered the effects of including or excluding predictors that are directly related to the respective inventory-based bias. Mixed-effects logistic regression was used to account for variation that was due to an inventory-based incompleteness.The results show that most erroneous predictions, but highest predictive performances, were obtained from models generated with highly incomplete inventories and predictors that were able to directly describe the respective incompleteness. An exclusion of such bias-describing predictors led to systematically confounded relationships. The application of mixed-effects models proved valuable to produce predictions that were least affected by inventory-based errors.This paper highlights that the degree of inventory-based incompleteness is only one of several aspects that determine how an inventory-based bias may propagate into the final results. We propose a four-step procedure to deal with incomplete inventories in the context of statistical landslide susceptibility modelling.  相似文献   

2.
Landslide susceptibility modelling—a crucial step towards the assessment of landslide hazard and risk—has hitherto not included the local, transient effects of previous landslides on susceptibility. In this contribution, we implement such transient effects, which we term “landslide path dependency”, for the first time. Two landslide path dependency variables are used to characterise transient effects: a variable reflecting how likely it is that an earlier landslide will have a follow-up landslide and a variable reflecting the decay of transient effects over time. These two landslide path dependency variables are considered in addition to a large set of conditioning attributes conventionally used in landslide susceptibility. Three logistic regression models were trained and tested fitted to landslide occurrence data from a multi-temporal landslide inventory: (1) a model with only conventional variables, (2) a model with conventional plus landslide path dependency variables, and (3) a model with only landslide path dependency variables. We compare the model performances, differences in the number, coefficient and significance of the selected variables, and the differences in the resulting susceptibility maps. Although the landslide path dependency variables are highly significant and have impacts on the importance of other variables, the performance of the models and the susceptibility maps do not substantially differ between conventional and conventional plus path dependent models. The path dependent landslide susceptibility model, with only two explanatory variables, has lower model performance, and differently patterned susceptibility map than the two other models. A simple landslide susceptibility model using only DEM-derived variables and landslide path dependency variables performs better than the path dependent landslide susceptibility model, and almost as well as the model with conventional plus landslide path dependency variables—while avoiding the need for hard-to-measure variables such as land use or lithology. Although the predictive power of landslide path dependency variables is lower than those of the most important conventional variables, our findings provide a clear incentive to further explore landslide path dependency effects and their potential role in landslide susceptibility modelling.  相似文献   

3.
In many regions, the absence of a landslide inventory hampers the production of susceptibility or hazard maps. Therefore, a method combining a procedure for sampling of landslide-affected and landslide-free grid cells from a limited landslide inventory and logistic regression modelling was tested for susceptibility mapping of slide- and flow-type landslides on a European scale. Landslide inventories were available for Norway, Campania (Italy), and the Barcelonnette Basin (France), and from each inventory, a random subsample was extracted. In addition, a landslide dataset was produced from the analysis of Google Earth images in combination with the extraction of landslide locations reported in scientific publications. Attention was paid to have a representative distribution of landslides over Europe. In total, the landslide-affected sample contained 1,340 landslides. Then a procedure to select landslide-free grid cells was designed taking account of the incompleteness of the landslide inventory and the high proportion of flat areas in Europe. Using stepwise logistic regression, a model including slope gradient, standard deviation of slope gradient, lithology, soil, and land cover type was calibrated. The classified susceptibility map produced from the model was then validated by visual comparison with national landslide inventory or susceptibility maps available from literature. A quantitative validation was only possible for Norway, Spain, and two regions in Italy. The first results are promising and suggest that, with regard to preparedness for and response to landslide disasters, the method can be used for urgently required landslide susceptibility mapping in regions where currently only sparse landslide inventory data are available.  相似文献   

4.
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.  相似文献   

5.
The identification of landslide-prone areas is an essential step in landslide hazard assessment and mitigation of landslide-related losses.In this study,we applied two novel deep learning algorithms,the recurrent neural network(RNN)and convolutional neural network(CNN),for national-scale landslide susceptibility mapping of Iran.We prepared a dataset comprising 4069 historical landslide locations and 11 conditioning factors(altitude,slope degree,profile curvature,distance to river,aspect,plan curvature,distance to road,distance to fault,rainfall,geology and land-sue)to construct a geospatial database and divided the data into the training and the testing dataset.We then developed RNN and CNN algorithms to generate landslide susceptibility maps of Iran using the training dataset.We calculated the receiver operating characteristic(ROC)curve and used the area under the curve(AUC)for the quantitative evaluation of the landslide susceptibility maps using the testing dataset.Better performance in both the training and testing phases was provided by the RNN algorithm(AUC=0.88)than by the CNN algorithm(AUC=0.85).Finally,we calculated areas of susceptibility for each province and found that 6%and 14%of the land area of Iran is very highly and highly susceptible to future landslide events,respectively,with the highest susceptibility in Chaharmahal and Bakhtiari Province(33.8%).About 31%of cities of Iran are located in areas with high and very high landslide susceptibility.The results of the present study will be useful for the development of landslide hazard mitigation strategies.  相似文献   

6.
Landslide susceptibility zonation mapping is a fundamental procedure for geo-disaster management in tropical and sub-tropical regions. Recently, various landslide susceptibility zonation models have been introduced in Nepal with diverse approaches of assessment. However, validation is still a problem. Additionally, the role of various predisposing causative parameters for landslide activity is still not well understood in the Nepal Himalaya. To address these issues of susceptibility zonation and landslide activity, about 4,000 km2 area of central Nepal was selected for regional-scale assessment of landslide activity and susceptibility zonation mapping. In total, 655 new landslides and 9,229 old landslides were identified with the study area with the help of satellite images, aerial photographs, field data and available reports. The old landslide inventory was “blind landslide database” and could not explain the particular rainfall event responsible for the particular landslide. But considering size of the landslide, blind landslide inventory was reclassified into two databases: short-duration high-intensity rainfall-induced landslide inventory and long-duration low-intensity rainfall-induced landslide inventory. These landslide inventory maps were considered as proxy maps of multiple rainfall event-based landslide inventories. Similarly, all 9,884 landslides were considered for the activity assessment of predisposing causative parameters. For the Nepal Himalaya, slope, slope aspect, geology and road construction activity (anthropogenic cause) were identified as most affective predisposing causative parameters for landslide activity. For susceptibility zonation, multivariate approach was considered and two proxy rainfall event-based landslide databases were used for the logistic regression modelling, while a relatively recent landslide database was used in validation. Two event-based susceptibility zonation maps were merged and rectified to prepare the final susceptibility zonation map and its prediction rate was found to be more than 82 %. From this work, it is concluded that rectification of susceptibility zonation map is very appropriate and reliable. The results of this research contribute to a significant improvement in landslide inventory preparation procedure, susceptibility zonation mapping approaches as well as role of various predisposing causative parameters for the landslide activity.  相似文献   

7.
Landslide susceptibility mapping is a vital tool for disaster management and planning development activities in mountainous terrains of tropical and subtropical environments. In this paper, the weights-of-evidence modelling was applied, within a geographical information system (GIS), to derive landslide susceptibility map of two small catchments of Shikoku, Japan. The objective of this paper is to evaluate the importance of weights-of-evidence modelling in the generation of landslide susceptibility maps in relatively small catchments having an area less than 4 sq km. For the study area in Moriyuki and Monnyu catchments, northeast Shikoku Island in west Japan, a data set was generated at scale 1:5,000. Relevant thematic maps representing various factors (e.g. slope, aspect, relief, flow accumulation, soil depth, soil type, land use and distance to road) that are related to landslide activity were generated using field data and GIS techniques. Both catchments have homogeneous geology and only consist of Cretaceous granitic rock. Thus, bedrock geology was not considered in data layering during GIS analysis. Success rates were also estimated to evaluate the accuracy of landslide susceptibility maps and the weights-of-evidence modelling was found useful in landslide susceptibility mapping of small catchments.  相似文献   

8.
 A shallow landslide erosion and sediment yield component, applicable at the basin scale, has been incorporated into the physically based, spatially distributed, hydrological and sediment transport modelling system, SHETRAN. The component determines when and where landslides occur in a basin in response to time-varying rainfall and snowmelt, the volume of material eroded and released for onward transport, and the impact on basin sediment yield. Derived relationships are used to link the SHETRAN grid resolution (up to 1 km), at which the basin hydrology and final sediment yield is modelled, to a subgrid resolution (typically around 10–100 m) at which landslide occurrence and erosion is modelled. The subgrid discretization, landslide susceptibility and potential landslide impact are determined in advance using a geographic information system (GIS), with SHETRAN then providing information on temporal variation in the factors controlling landsliding. The ability to simulate landslide sediment yield is demonstrated by a hypothetical application based on a catchment in Scotland. Received: 30 October 1996 · Accepted: 25 June 1997  相似文献   

9.
Landslides are one of the most frequent and common natural hazards in many parts of Himalaya. To reduce the potential risk, the landslide susceptibility maps are one of the first and most important steps in the landslide hazard mitigation. Earth observation satellite and geographical information system-based techniques have been used to derive and analyse various geo-environmental parameters significant to landslide hazards. In this study, a bivariate statistics method was used for spatial modelling of landslide susceptibility zones. For this purpose, thematic layers including landslide inventory, geology, slope angle, slope aspect, geomorphology, slope morphology, drainage density, lineament and land use/land cover were used. A large number of landslide occurrences have been observed in the upper Tons river valley area of Western Himalaya. The result has been used to spatially classify the study area into zones of very high, high, moderate, low and very low landslide susceptibility zones. About 72% of active landslides have been observed to occur in very high and high hazard zones. The result of the analysis was verified using the landslide location data. The validation result shows significant agreement between the susceptibility map and landslide location. The result can be used to reduce landslide hazards by proper planning.  相似文献   

10.
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.  相似文献   

11.
Landslide risk assessment is based on spatially integrating landslide hazard with exposed elements-at-risk to determine their vulnerability and to express the expected direct and indirect losses. There are three components that are relevant for expressing landslide hazard: spatial, temporal, and magnitude probabilities. At a medium-scale analysis, this is often done by first deriving a landslide susceptibility map, and to determine the three types of probabilities on the basis of landslide inventories linked to particular triggering events. The determination of spatial, temporal, and magnitude probabilities depend mainly on the availability of sufficiently complete historical records of past landslides, which in general are rare in most countries (e.g., India, etc.). In this paper, we presented an approach to use available historical information on landslide inventories for landslide hazard and risk analysis on a medium scale (1:25,000) in a perennially typical data-scarce environment in Darjeeling Himalayas (India). We demonstrate how the incompleteness in the resulting landslide database influences the various components in the calculation of specific risk of elements-at-risk (e.g., buildings, population, roads, etc.). We incorporate the uncertainties involved in the risk estimation and illustrate the range of expected losses in the form of maximum and minimum loss curves. The study demonstrates that even in data-scarce environments, quantitative landslide risk assessment is a viable option, as long as the uncertainties involved are expressed.  相似文献   

12.
The main purpose of this paper is to present the use of multi-resource remote sensing data, an incomplete landslide inventory, GIS technique and logistic regression model for landslide susceptibility mapping related to the May 12, 2008 Wenchuan earthquake of China. Landslide location polygons were delineated from visual interpretation of aerial photographs, satellite images in high resolutions, and verified by selecting field investigations. Eight factors, including slope angle, slope aspect, elevation, distance from drainages, distance from roads, distance from main faults, seismic intensity and lithology were selected as controlling factors for earthquake-triggered landslide susceptibility mapping. Qualitative susceptibility analyses were carried out using the map overlaying techniques in GIS platform. The validation result showed a success rate of 82.751 % between the susceptibility probability index map and the location of the initial landslide inventory. The predictive rate of 86.930 % was obtained by comparing the additional landslide polygons and the landslide susceptibility probability index map. Both the success rate and the predictive rate show sufficient agreement between the landslide susceptibility map and the existing landslide data, and good predictive power for spatial prediction of the earthquake-triggered landslides.  相似文献   

13.
As threats of landslide hazards have become gradually more severe in recent decades,studies on landslide prevention and mitigation have attracted widespread attention in relevant domains.A hot research topic has been the ability to predict landslide susceptibility,which can be used to design schemes of land exploitation and urban development in mountainous areas.In this study,the teaching-learning-based optimization(TLBO)and satin bowerbird optimizer(SBO)algorithms were applied to optimize the adaptive neuro-fuzzy inference system(ANFIS)model for landslide susceptibility mapping.In the study area,152 landslides were identified and randomly divided into two groups as training(70%)and validation(30%)dataset.Additionally,a total of fifteen landslide influencing factors were selected.The relative importance and weights of various influencing factors were determined using the step-wise weight assessment ratio analysis(SWARA)method.Finally,the comprehensive performance of the two models was validated and compared using various indexes,such as the root mean square error(RMSE),processing time,convergence,and area under receiver operating characteristic curves(AUROC).The results demonstrated that the AUROC values of the ANFIS,ANFIS-TLBO and ANFIS-SBO models with the training data were 0.808,0.785 and 0.755,respectively.In terms of the validation dataset,the ANFISSBO model exhibited a higher AUROC value of 0.781,while the AUROC value of the ANFIS-TLBO and ANFIS models were 0.749 and 0.681,respectively.Moreover,the ANFIS-SBO model showed lower RMSE values for the validation dataset,indicating that the SBO algorithm had a better optimization capability.Meanwhile,the processing time and convergence of the ANFIS-SBO model were far superior to those of the ANFIS-TLBO model.Therefore,both the ensemble models proposed in this paper can generate adequate results,and the ANFIS-SBO model is recommended as the more suitable model for landslide susceptibility assessment in the study area considered due to its excellent accuracy and efficiency.  相似文献   

14.
Preparation of landslide susceptibility maps is considered as the first important step in landslide risk assessments, but these maps are accepted as an end product that can be used for land use planning. The main objective of this study is to explore some new state-of-the-art sophisticated machine learning techniques and introduce a framework for training and validation of shallow landslide susceptibility models by using the latest statistical methods. The Son La hydropower basin (Vietnam) was selected as a case study. First, a landslide inventory map was constructed using the historical landslide locations from two national projects in Vietnam. A total of 12 landslide conditioning factors were then constructed from various data sources. Landslide locations were randomly split into a ratio of 70:30 for training and validating the models. To choose the best subset of conditioning factors, predictive ability of the factors were assessed using the Information Gain Ratio with 10-fold cross-validation technique. Factors with null predictive ability were removed to optimize the models. Subsequently, five landslide models were built using support vector machines (SVM), multi-layer perceptron neural networks (MLP Neural Nets), radial basis function neural networks (RBF Neural Nets), kernel logistic regression (KLR), and logistic model trees (LMT). The resulting models were validated and compared using the receive operating characteristic (ROC), Kappa index, and several statistical evaluation measures. Additionally, Friedman and Wilcoxon signed-rank tests were applied to confirm significant statistical differences among the five machine learning models employed in this study. Overall, the MLP Neural Nets model has the highest prediction capability (90.2 %), followed by the SVM model (88.7 %) and the KLR model (87.9 %), the RBF Neural Nets model (87.1 %), and the LMT model (86.1 %). Results revealed that both the KLR and the LMT models showed promising methods for shallow landslide susceptibility mapping. The result from this study demonstrates the benefit of selecting the optimal machine learning techniques with proper conditioning selection method in shallow landslide susceptibility mapping.  相似文献   

15.
Crucial to most landslide early warning system (EWS) is the precise prediction of rainfall in space and time. Researchers are aware of the importance of the spatial variability of rainfall in landslide studies. Commonly, however, it is neglected by implementing simplified approaches (e.g. representative rain gauges for an entire area). With spatially differentiated rainfall information, real-time comparison with rainfall thresholds or the implementation in process-based approaches might form the basis for improved landslide warnings. This study suggests an automated workflow from the hourly, web-based collection of rain gauge data to the generation of spatially differentiated rainfall predictions based on deterministic and geostatistical methods. With kriging usually being a labour-intensive, manual task, a simplified variogram modelling routine was applied for the automated processing of up-to-date point information data. Validation showed quite satisfactory results, yet it also revealed the drawbacks that are associated with univariate geostatistical interpolation techniques which solely rely on rain gauges (e.g. smoothing of data, difficulties in resolving small-scale, highly intermittent rainfall). In the perspective, the potential use of citizen scientific data is highlighted for the improvement of studies on landslide EWS.  相似文献   

16.
Four statistical techniques for modelling landslide susceptibility were compared: multiple logistic regression (MLR), multivariate adaptive regression splines (MARS), classification and regression trees (CART), and maximum entropy (MAXENT). According to the literature, MARS and MAXENT have never been used in landslide susceptibility modelling, and CART has been used only twice. Twenty independent variables were used as predictors, including lithology as a categorical variable. Two sets of random samples were used, for a total of 90 model replicates (with and without lithology, and with different proportions of positive and negative data). The model performance was evaluated using the area under the receiver operating characteristic curve (AUC) statistic. The main results are (a) the inclusion of lithology improves the model performance; (b) the best AUC values for single models are MLR (0.76), MARS (0.76), CART (0.77), and MAXENT (0.78); (c) a smaller amount of negative data provides better results; (d) the models with the highest prediction capability are obtained with MAXENT and CART; and (e) the combination of different models is a way to evaluate the model reliability. We further discuss some key issues in landslide modelling, including the influence of the various methods that we used, the sample size, and the random replicate procedures.  相似文献   

17.
The main objective of this study is to investigate potential application of frequency ratio (FR), weights of evidence (WoE), and statistical index (SI) models for landslide susceptibility mapping in a part of Mazandaran Province, Iran. First, a landslide inventory map was constructed from various sources. The landslide inventory map was then randomly divided in a ratio of 70/30 for training and validation of the models, respectively. Second, 13 landslide conditioning factors including slope degree, slope aspect, altitude, plan curvature, stream power index, topographic wetness index, sediment transport index, topographic roughness index, lithology, distance from streams, faults, roads, and land use type were prepared, and the relationships between these factors and the landslide inventory map were extracted by using the mentioned models. Subsequently, the multi-class weighted factors were used to generate landslide susceptibility maps. Finally, the susceptibility maps were verified and compared using several methods including receiver operating characteristic curve with the areas under the curve (AUC), landslide density, and spatially agreed area analyses. The success rate curve showed that the AUC for FR, WoE, and SI models was 81.51, 79.43, and 81.27, respectively. The prediction rate curve demonstrated that the AUC achieved by the three models was 80.44, 77.94, and 79.55, respectively. Although the sensitivity analysis using the FR model revealed that the modeling process was sensitive to input factors, the accuracy results suggest that the three models used in this study can be effective approaches for landslide susceptibility mapping in Mazandaran Province, and the resultant susceptibility maps are trustworthy for hazard mitigation strategies.  相似文献   

18.
空间三维滑坡敏感性分区工具及其应用   总被引：1，自引：0，他引：1  
对于滑坡敏感性分区目前有三种方法:定性法、统计法和基于岩土定量模型的确定性方法。定性法基于对滑坡敏感性或灾害评估的人为判断;统计法用一个来源于结合了权重因子的预测函数或指标;而确定性法,或者说是物理定量模型法以质量、能量和动量守恒定律为基础。二维确定性模型广泛用于土木工程设计,而无限边坡模型(一维)也用于滑坡灾害分区的确定性模型。文中提出了一个新的基于GIS(地理信息系统)的滑坡敏感性分区系统,这个系统可用于从复杂地形中确认可能的危险三维(3-D)滑坡体。所有与滑坡相关的空间数据(矢量或栅格数据)都被集成到这个系统中。通过把研究区域划分为边坡单元并假定初始滑动面是椭球的下半部分,并使用Monte Carlo随机搜索法,三维滑坡稳定性分析中的三维最危险滑面是三维安全系数最小的地方。使用近似方法假定有效凝聚力、有效摩擦角和三维安全系数服从正态分布,可以计算出滑坡失稳概率。3DSlopeGIS是一个计算机程序,它内嵌了GIS Developer kit(ArcObjects of ESRI)来实现GIS空间分析功能和有效的数据管理。应用此工具可以解决所有的三维边坡空间数据解问题。通过使用空间分析、数据管理和GIS的可视化功能来处理复杂的边坡数据,三维边坡稳定性问题很容易用一个友好的可视化图形界面来解决。将3DSlopeGIS系统应用到3个滑坡敏感性分区的实例中:第一个是一个城市规划项目,第二个是预测以往滑坡灾害对临近区域可能的影响,第三个则是沿着国家主干道的滑坡分区。基于足够次数的Monte Carlo模拟法,可以确认可能的最危险滑坡体。这在以往的传统边坡稳定性分析中是不可能的。  相似文献   

19.
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.  相似文献   

20.
Landslides are natural geological disasters causing massive destructions and loss of lives, as well as severe damage to natural resources, so it is essential to delineate the area that probably will be affected by landslides. Landslide susceptibility mapping (LSM) is making increasing implications for GIS-based spatial analysis in combination with multi-criteria evaluation (MCE) methods. It is considered to be an effective tool to understand natural disasters related to mass movements and carry out an appropriate risk assessment. This study is based on an integrated approach of GIS and statistical modelling including fuzzy analytical hierarchy process (FAHP), weighted linear combination and MCE models. In the modelling process, eleven causative factors include slope aspect, slope, rainfall, geology, geomorphology, distance from lineament, distance from drainage networks, distance from the road, land use/land cover, soil erodibility and vegetation proportion were identified for landslide susceptibility mapping. These factors were identified based on the (1) literature review, (2) the expert knowledge, (3) field observation, (4) geophysical investigation, and (5) multivariate techniques. Initially, analytical hierarchy process linked with the fuzzy set theory is used in pairwise comparisons of LSM criteria for ranking purposes. Thereafter, fuzzy membership functions were carried out to determine the criteria weights used in the development of a landslide susceptibility map. These selected thematic maps were integrated using a weighted linear combination method to create the final landslide susceptibility map. Finally, a validation of the results was carried out using a sensitivity analysis based on receiver operator curves and an overlay method using the landslide inventory map. The study results show that the weighted overlay analysis method using the FAHP and eigenvector method is a reliable technique to map landslide susceptibility areas. The landslide susceptibility areas were classified into five categories, viz. very low susceptibility, low susceptibility, moderate susceptibility, high susceptibility, and very high susceptibility. The very high and high susceptibility zones account for 15.11% area coverage. The results are useful to get an impression of the sustainability of the watershed in terms of landsliding and therefore may help decision makers in future planning and mitigation of landslide impacts.  相似文献   

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