Application of expert rules in indirect approaches for landslide susceptibility assessment

Landslide susceptibility (LS) assessment by indirect approaches presents some limitations due to (1) the tendency to simplify the environmental factors (i.e., variables) and (2) the assumptions that landslides occur under the same combination of variables for a study site. Recently, some authors have discussed the interest to introduce expert knowledge in the indirect approaches in order to improve the quality of indirect LS maps. However, if the results are reliable, the procedures used seem fastidious and a very good knowledge of the study site is essential. The objectives of this paper are to discuss a methodology to introduce the expert knowledge in the indirect mapping process. After the definition of the expert rules associated to three landslide types, several indirect LS maps are produced by two indirect exploratory approaches, based on fuzzy set theory and on a modification of a bivariate method called expert weight of evidence. Then, the indirect LS maps are confronted to a landslide inventory and a LS map produced by a direct approach. The analyses indicate that the methodology used to introduce the expert rules in the mapping process increases the predictive power of indirect LS map. Finally, some indications about advantages and drawbacks of each approach are given to help the geoscientist to introduce his expert knowledge in the landslide susceptibility mapping process.     

基于滑坡分类的西宁市滑坡易发性评价

以往的滑坡易发性评价多以全体滑坡为研究对象,忽视了滑坡类型的区别。各评价指标对不同类型滑坡的影响程度不同,也导致指标权重无法精确地反映其对滑坡的影响。为更准确地对滑坡灾害进行空间预测,针对西宁市滑坡特征及发育机理,将全区滑坡分为土质滑坡和岩质滑坡;在野外实际调查的基础上,结合相关性分析,选取坡度、坡向、剖面曲率、平面曲率、工程地质岩组,以及滑坡点距断层、水系、道路的距离远近等8项因素作为滑坡易发性评价指标,并通过滑坡点分布密度和滑坡点相对分布密度,分析各评价指标分别对土质滑坡和岩质滑坡的影响;利用信息量模型,计算各评价指标对两类滑坡的信息量值,利用人工神经网络模型,赋予各评价指标对两类滑坡的权重;最后基于GIS平台利用加权信息量模型对研究区进行易发性评价。通过统计方法和ROC曲线法分别计算滑坡易发性评价成功率,结果表明:评价成功率可达到82.61%和82.30%,与未经滑坡分类的成功率比较,分别提高了10.9%和5.2%;同时,经过滑坡分类后,湟水河两岸地质条件较差的地区转变为滑坡高易发区。     

A small-scale landslide susceptibility assessment for the territory of Western Carpathians

This study presented herein compares the bivariate and multivariate landslide susceptibility mapping methods and presents the landslide susceptibility map of the territory of Western Carpathians in small scale. This study also describes pioneer work for the territory of Western Carpathians, overreaching state borders, using verified sophisticated statistical methods. In the susceptibility mapping, digital elevation model was first constructed using a GIS software, and parameter maps affecting the slope stability such as geology, seismicity, precipitation, topographical elevation, slope angle, slope aspect and land cover were considered. In the last stage of the analyses, landslide susceptibility maps were produced using bivariate and multivariate analyses, and they were then compared by means of their validations. The validation of the bivariate analysis data was performed using the results of bivariate analysis for landslide areas of Slovakia containing five classes of susceptibility in scale 1:500,000. The validation area is the area of Western Carpathians within Slovakia. Eighty-two per cent of area does not differ in more than one class. The validation of the multivariate analysis data was performed using the results from the Kysuce region in the northern part of Slovakia in scale 1:10,000. The raster calculator was used to express the difference between each pair of pixels within these two layers. Seventy-seven per cent of the pixels do not differ in more than 25 %, 94 % of the pixels do not differ in more than 50 %. The maximal possible difference is 100 % (one pixel with value 0 and other with value 1, or vice versa). Receiver operating characteristic analysis was also performed, the area under curve value for bivariate model was calculated to be 0.735, while it was 0.823 for multivariate. The results of the validation can be considered as satisfactory.     

区域性滑坡敏感性评价的数据驱动权重模型及应用

基于GIS的区域滑坡灾害评价的空间分析方法主要有两种,一是基于专家经验的知识驱动型方法;二是基于统计学的数据驱动型方法.前者对于影响滑坡灾害发生的评价指标的分级以及权重的确定多依赖于专家的经验,具有主观性和不确定性.而后者则是根据已发生滑坡灾害的历史调查数据以及影响滑坡灾害发生的孕灾环境因子资料,对评价指标进行分级和权重的确定.后者使区域滑坡评价更具科学性和可靠性.本文基于GIS技术,运用贝叶斯统计方法的数据驱动权重模型及其分析程序,进行了哥伦比亚Chinchina地区滑坡灾害敏感性分析.     

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。     

Implementing landslide path dependency in landslide susceptibility modelling

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.     

Spatial data for landslide susceptibility, hazard, and vulnerability assessment: An overview

The aim of this paper is to discuss a number of issues related to the use of spatial information for landslide susceptibility, hazard, and vulnerability assessment. The paper centers around the types of spatial data needed for each of these components, and the methods for obtaining them. A number of concepts are illustrated using an extensive spatial data set for the city of Tegucigalpa in Honduras. The paper intends to supplement the information given in the “Guidelines for Landslide Susceptibility, Hazard and Risk Zoning for Land Use Planning” by the Joint ISSMGE, ISRM and IAEG Technical Committee on Landslides and Engineered Slopes (JTC-1). The last few decades have shown a very fast development in the application of digital tools such as Geographic Information Systems, Digital Image Processing, Digital Photogrammetry and Global Positioning Systems. Landslide inventory databases are becoming available to more countries and several are now also available through the internet. A comprehensive landslide inventory is a must in order to be able to quantify both landslide hazard and risk. With respect to the environmental factors used in landslide hazard assessment, there is a tendency to utilize those data layers that are easily obtainable from Digital Elevation Models and satellite imagery, whereas less emphasis is on those data layers that require detailed field investigations. A review is given of the trends in collecting spatial information on environmental factors with a focus on Digital Elevation Models, geology and soils, geomorphology, land use and elements at risk.     

Effect of using various weighting methods in a process of landslide susceptibility assessment

Natural Hazards - This study discusses the evaluation of the effect of using different weighting approaches in the process of landslide susceptibility assessment. Weighting process is needed,...     

Soft computing and GIS for landslide susceptibility assessment in Tawaghat area, Kumaon Himalaya, India

This paper mainly presents a case study of landslide vulnerability zonation along Tawaghat-Mangti route corridor in Kumaon Himalaya, India. An attempt is made to predict landslide susceptibility using back-propagation neural network (BPNN) and propose a suitable model for that zone, which can be successfully implemented for the prevention of slides. Various landslide affecting parameters such as lithology, slope, aspect, structure, geotechnical properties, land use, landslide inventory, and distance from recorded epicenter are used to model the landslide susceptibility. The database on the above parameters derived from satellite imageries, topographic maps, and field work are integrated in the GIS to generate an information layer. Database of this information layer is used to train, test, and validate the BPNN model. A three-layered BPNN with an input layer, two hidden layers, and one output layer is found to be optimal. The developed model demonstrates a promising result, and the prediction accuracy has been found to be 80?% in the field.     

基于分形的滑坡易发程度区划方法

本文发展了一种基于分形统计的滑坡易发程度评价方法,该方法仅使用已有的滑坡数据,首先通过分形统计获得滑坡分布的分形丛集关系,再通过GIS的空间操作与分析生成滑坡易发程度区划图。提出一种对滑坡易发程度区划图的可信度和预测效果进行评价的方法。本文介绍了这些方法及其在浙江地区应用的结果。     

基于贝叶斯优化机器学习超参数的滑坡易发性评价

利用机器学习模型进行滑坡易发性评价时,不同的超参数设置往往会导致评价结果的不同.采用贝叶斯算法对4种常见机器学习模型(逻辑回归LR、支持向量机SVM、人工神经网络ANN和随机森林RF)的超参数进行了优化,探索了该算法对滑坡易发性机器学习模型的优化效果.以湘中地区4县(安化县、新华县、桃江县和桃源县)滑坡易发性评价为例说...     

A comparison of slope units and grid cells as mapping units for landslide susceptibility assessment

Landslides lead to a great threat to human life and property safety. The delineation of landslide-prone areas achieved by landslide susceptibility assessment plays an important role in landslide management strategy. Selecting an appropriate mapping unit is vital for landslide susceptibility assessment. This paper compares the slope unit and grid cell as mapping unit for landslide susceptibility assessment. Grid cells can be easily obtained and their matrix format is convenient for calculation. A slope unit is considered as the watershed defined by ridge lines and valley lines based on hydrological theory and slope units are more associated with the actual geological environment. Using 70% landslide events as the training data and the remaining landslide events for verification, landslide susceptibility maps based on slope units and grid cells were obtained respectively using a modified information value model. ROC curve was utilized to evaluate the landslide susceptibility maps by calculating the training accuracy and predictive accuracy. The training accuracies of the grid cell-based susceptibility assessment result and slope unit-based susceptibility assessment result were 80.9 and 83.2%, and the prediction accuracies were 80.3 and 82.6%, respectively. Therefore, landslide susceptibility mapping based on slope units performed better than grid cell-based method.     

GIS-based assessment of landslide susceptibility on the base of the Weights-of-Evidence model

The major scope of the study is the assessment of landslide susceptibility of Flysch areas including the Penninic Klippen in the Vienna Forest (Lower Austria) by means of Geographical Information System (GIS)-based modelling. A statistical/probabilistic method, referred to as Weights-of-Evidence (WofE), is applied in a GIS environment in order to derive quantitative spatial information on the predisposition to landslides. While previous research in this area concentrated on local geomorphological, pedological and slope stability analyses, the present study is carried out at a regional level. The results of the modelling emphasise the relevance of clay shale zones within the Flysch formations for the occurrence of landslides. Moreover, the distribution of mass movements is closely connected to the fault system and nappe boundaries. An increased frequency of landslides is observed in the proximity to drainage lines, which can change to torrential conditions after heavy rainfall. Furthermore, landslide susceptibility is enhanced on N-W facing slopes, which are exposed to the prevailing direction of wind and rainfall. Both of the latter geofactors indirectly show the major importance of the hydrological conditions, in particular, of precipitation and surface runoff, for the occurrence of mass movements in the study area. Model performance was checked with an independent validation set of landslides, which are not used in the model. An area of 15% of the susceptibility map, classified as highly susceptible, “predicted” 40% of the landslides.     

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.     

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.     

基于GIS的哀牢山戛洒镇小流域滑坡易发性评价

根据研究区的基本情况,选择坡度、坡向、地层岩性、距断层距离、降雨、土地利用等6个评价因子,采用滑坡灾害易发性评价的GIS与AHP耦合模型进行戛洒镇滑坡灾害易发性评价,并将滑坡灾害分为极高、高、中、低和极低易发区5个区域进行了滑坡灾害易发性评价结果分析,以期为后期的小流域滑坡风险评估研究服务。