GIS analysis to assess landslide susceptibility in a fluvial basin of NW Sicily (Italy)

Landslide hazard assessment, effected by means of geostatistical methods, is based on the analysis of the relationships between landslides and the spatial distributions of some instability factors. Frequently such analyses are based on landslide inventories in which each record represents the entire unstable area and is managed as a single instability landform. In this research, landslide susceptibility is evaluated through the study of a variety of instability landforms: landslides, scarps and areas uphill from crown. The instability factors selected were: bedrock lithology, steepness, topographic wetness index and stream power index. The instability landform densities computed for all the factors, which were arranged in Unique Condition Unit, allowed us to derive a total of three prediction images for each landslide typology. The role of the instability factors and the effects generated by the use of different landforms were analyzed by means of: a) bivariate analysis of the relationships between factors and landslide density; b) predictive power validations of the prediction images, based on a random partition strategy.The test area was the Iato River Basin (North-Western Sicily), whose slopes are moderately involved in flow and rotational slide landslides (219 and 28, respectively). The area is mainly made up of the following complexes: Numidian Flysch clays (19%, 1%), Terravecchia sandy clays (5%, 1%), Terravecchia clayey sands (3%, 0.3%) and San Cipirello marly clays (9%, 0%). The steepness parameter shows the highest landslide density in the [11–19°] class for both the typologies (8%, 1%), even if the density distributions for rotational slides are right-asymmetric and right-shifted. We obtained significant differences in shape when we used different instability landforms. Unlike scarps and areas uphill from crowns, landslide areas produce left-asymmetric and left-shifted density distributions for both the typologies. As far as the topographic wetness index is concerned, much more pronounced differences were detected among the instability landforms of rotational slides. In contrast, the flow landslides produce normal-like density distributions. The latter and the rotational slide landslide areas produce the highest density values in the class [5.5–6.7], despite an abrupt decreasing trend starting from the first class [3.2–4.4], which is generated by the density values of the rotational slide scarps and areas uphill from crowns. The stream power index at the foot of the slopes, which was automatically derived using a GIS-procedure, shows a positive correlation with the landslide densities marked by the maximum classes: [4.8–6.0] for flows, and [6.0–7.2] for rotational slides. The validation procedure results confirmed that the choice of instability landform influences the results of the susceptibility analysis. Furthermore, the validation procedure indicates that: a) the predictive models are generally satisfactory; b) scarps and zones uphill from crown areas are the most diagnostically unstable landforms, for flow and rotational slide landslides respectively.     

GIS and ANN model for landslide susceptibility mapping

1 IntroductionThe population growth and the expansion of settlements and life-lines over hazardous areas exert increasingly great impact of natural disasters both in the developed and developing countries. In many countries, the economic losses and casualties due to landslides are greater than commonly recognized and generate a yearly loss of property larger than that from any other natural disasters, including earthquakes, floods and windstorms. Landslides in mountainous terrain often occur a…     

GIS and ANN model for landslide susceptibility mapping

Landslide hazard is as the probability of occurrence of a potentially damaging landslide phenomenon within specified period of time and within a given area. The susceptibility map provides the relative spatial probability of landslides occurrence. A study is presented of the application of GIS and artificial neural network model to landslide susceptibility mapping, with particular reference to landslides on natural terrain in this paper. The method has been applied to Lantau Island, the largest outlying island within the territory of Hong Kong. A three-level neural network model was constructed and trained by the back-propagate algorithm in the geographical database of the study area. The data in the database includes digital elevation modal and its derivatives, landslides distribution and their attributes, superficial geological maps, vegetation cover, the raingauges distribution and their 14 years 5-minute observation. Based on field inspection and analysis of correlation between terrain variables and landslides frequency, lithology, vegetation cover, slope gradient, slope aspect, slope curvature, elevation, the characteristic value, the rainstorms corresponding to the landslide, and distance to drainage line are considered to be related to landslide susceptibility in this study. The artificial neural network is then coupled with the ArcView3.2 GIS software to produce the landslide susceptibility map, which classifies the susceptibility into three levels: low, moderate, and high. The results from this study indicate that GIS coupled with artificial neural network model is a flexible and powerful approach to identify the spatial probability of hazards.     

Seven landslide dams of old seismic origin in southeastern Sicily (Italy)

This paper focuses on origin, morphology and evolution of seven landslide dams in southeastern Sicily. These landforms are part of a set of 146 landslides recently recognised in this area, which was hitherto considered to have little or no slope instability. Southeastern Sicily consists of a plateau (the Hyblaean Mountains) incised by canyons and surrounded by lower lands. It is underlain mostly by subhorizontal, moderately to well-lithified carbonate rocks. Relief is low.Several lines of evidence justify the assumption of a seismic trigger for the landslides in this area: (1) the geo-climatic environment is not favourable to landsliding, (2) low-angle basal shear surfaces are very frequent, (3) landslide distribution is consistent with the known magnitude–distance relationships for earthquake-induced landslides, (4) historical documents testify to earthquake-triggered slope instability and (5) a specific landslide can be exactly dated.The phenomena illustrated here include six rock slides (one with a debris-flow component) and one rock fall. Slip surfaces are mostly non-circular. Landslide volume ranges from about 50×10³ to 34×10⁶ m³.With reference to the Costa and Schuster [Geol. Soc. Am. Bull. 100 (1988) 1054] classification of landslide dams, five cases belong to type II (spanning the entire valley), and two to type IV (failures from both valley sides, with frontal or side contact between failed masses). With reference to Crozier and Pillans [Catena 18 (1991) 471] classification of landslide lakes, all cases show a main valley lake while tributary valley, back and supra lakes are sporadically present. One damming is attributable to the 1693 earthquake with certainty; another damming, to the same earthquake with high probability. Three dams were reincised, one breached or reincised, one is slightly reincised and two more or less intact; correspondingly, five silting up deposits were reincised, one is being reincised at present and two are still under formation.     

Prediction of landslide susceptibility using rare events logistic regression: A case-study in the Flemish Ardennes (Belgium)

In this article a statistical multivariate method, i.e., rare events logistic regression, is evaluated for the creation of a landslide susceptibility map in a 200 km² study area of the Flemish Ardennes (Belgium). The methodology is based on the hypothesis that future landslides will have the same causal factors as the landslides initiated in the past. The information on the past landslides comes from a landslide inventory map obtained by detailed field surveys and by the analysis of LIDAR (Light Detection and Ranging)-derived hillshade maps. Information on the causal factors (e.g., slope gradient, aspect, lithology, and soil drainage) was extracted from digital elevation models derived from LIDAR and from topographical, lithological and soil maps. In landslide-affected areas, however, we did not use the present-day hillslope gradient. In order to reflect the hillslope condition prior to landsliding, the pre-landslide hillslope was reconstructed and its gradient was used in the analysis. Because of their limited spatial occurrence, the landslides in the study area can be regarded as “rare events”. Rare events logistic regression differs from ordinary logistic regression because it takes into account the low proportion of 1s (landslides) to 0s (no landslides) in the study area by incorporating three correction measures: the endogenous stratified sampling of the dataset, the prior correction of the intercept and the correction of the probabilities to include the estimation uncertainty. For the study area, significant model results were obtained, with pre-landslide hillslope gradient and three different clayey lithologies being important predictor variables. Receiver Operating Characteristic (ROC) curves and the Kappa index were used to validate the model. Both show a good agreement between the observed and predicted values of the validation dataset. Based on a qualified judgement, the created landslide susceptibility map was classified into four classes, i.e., very high, high, moderate and low susceptibility. If interpreted correctly, this classified susceptibility map is an important tool for the delineation of zones where prevention measures are needed and human interference should be limited in order to avoid property damage due to landslides.     

Predictive landslide susceptibility assessment using the weight-of-evidence method in north-central Garhwal Himalaya,India

Landslides are frequent natural disasters in mountainous regions, particularly in the Himalayas in India during the southwest monsoon season. Although scientific study of landslides has been in progress for years, no significant achievement has been made to preclude landsliding and allay disasters. This research was undertaken to understand the areal distribution of landslides based on geological formations and geomorphological processes, and to provide more precise information regarding slope instability and mechanisms of failure. After completing a landslide inventory, prepared through field investigation and satellite image analysis, 493 landslides, comprising 131 investigated in the field and 362 identified from satellite imagery, were identified and mapped. The areal distribution of these landslides shows that sites more prone to landsliding have moderate to steep slopes, the lithology of the Lesser Himalayan formations, and excavations for road corridors. Landslide susceptibility zones were delineated for the area using the weight-of-evidence method on the basis of the frequency and distribution of landslides. Weights of selected variables were computed on the basis of severity of triggering factors. The accuracy of landslide susceptibility zones, calculated statistically (R² = .93), suggests high accuracy of the model for predicting landsliding in the area.     

Landslide susceptibility mapping for a part of tectonic Kelkit Valley (Eastern Black Sea region of Turkey)

Considering damage to man-made structures by natural hazards in Turkey, landslides are the second most important hazard after earthquakes. For this reason, a large-scale study titled Turkish Landslide Inventory Project, has been carried out since 1998. During this project, some special, susceptibility, hazard and risk assessments have been performed. In this study, a landslide susceptibility map of a part of tectonic Kelkit Valley in the north of central Turkey was produced, employing binary logistic regression analyses. To achieve the most appropriate results some sensitivity analyses were also carried out. For this purpose, four different data sets were constructed considering conditioning factors used and sampling strategies applied for the training data sets in this study. As a consequence of the analyses, the most proper outcomes were obtained by using the data set in which continuous topographical parameters and lithological dummy variables were implemented together and 50% of training data set was taken from seed cells at random. Correct classification percentage and Root Mean Square Error (RMSE) values for the validation data for that case were estimated as 84.16% and 0.36, respectively. This prediction capability shows that the landslide susceptibility map produced in this research paper can be used for the planning of protective and mitigation measures in the region.     

Landslide susceptibility evaluation based on BPNN and GIS: a case of Guojiaba in the Three Gorges Reservoir Area

A landslide susceptibility evaluation is vital for disaster management and development planning in the Yangtze River Three Gorges Reservoir Area. In this study, with the support of remote sensing and Geographic Information System, 4 factor groups comprising 10 separate subfactors of landslide-related data layers were selected to establish a susceptibility evaluation model based on the back-propagation neural network including slope, aspect, plan curvature, strata and lithology, distance to faults, land use/land cover, Normalized Difference Vegetation Index, Normalized Difference Water Index, distance from roads, and effect of rivers. During model development, a three-layered interconnected neural network structure of 10 (input layer) × 20 (hidden layer) × 1 (output layer) was used for evaluating the landslide susceptibility in Guojiaba. At the same time, a back-propagation algorithm was applied to calculate the weights between the input layer and the hidden layer and between the hidden layer and the output layer. The results showed that the effect of slope has the highest weight value (0.2051), which is more than two times that of the other factors, followed by strata and lithology (0.1213) and then the effect of rivers (0.1201). At the end of the susceptibility evaluation, the area was divided into four zones such as very high, high, moderate and low susceptibility. For verification, the receiver operating characteristic curve for the back-propagation neural network-derived landslide susceptibility evaluation model was drawn, and the results showed that the area under the receiver operating characteristic curve was 0.8790 and the prediction accuracy was 88%. Furthermore, the results obtained from this article were then verified by comparing with the existing landslide historical data and multiple field-verified results. Lastly, the landslide susceptibility map will help decision makers in risk management, site selection, site planning, and the design of control engineering.     

GIS支持下三峡库区秭归县滑坡灾害空间预测

基于GIS空间分析和统计模型相结合进行区域评价与空间预测是滑坡灾害研究的重要方向之一。以三峡库区秭归县为研究区,选择坡度、坡向、边坡结构、工程岩组、排水系统、土地利用和公路开挖作为评价因子。为提高模型的预测精度、可信度和推广能力,利用窗口采样规则降低训练样本之间的空间相关性。建立Logistic回归模型,对滑坡灾害与评价因子进行定量相关性分析。计算研究区滑坡灾害易发性指数,对其进行聚类分析,绘制滑坡易发性分区图,其中高、中易发区占整个研究区面积的38.9%,主要分布在人类工程活动频繁和靠近排水系统的区域。经过验证,该模型的预测精度达到77.57%。     

A procedure for landslide susceptibility zonation by the conditional analysis method

Numerous methods have been proposed for landslide probability zonation of the landscape by means of a Geographic Information System (GIS). Among the multivariate methods, i.e. those methods which simultaneously take into account all the factors contributing to instability, the Conditional Analysis method applied to a subdivision of the territory into Unique Condition Units is particularly straightforward from a conceptual point of view and particularly suited to the use of a GIS. In fact, working on the principle that future landslides are more likely to occur under those conditions which led to past instability, landslide susceptibility is defined by computing the landslide density in correspondence with different combinations of instability factors. The conceptual simplicity of this method, however, does not necessarily imply that it is simple to implement, especially as it requires rather complex operations and a high number of GIS commands. Moreover, there is the possibility that, in order to achieve satisfactory results, the procedure has to be repeated a few times changing the factors or modifying the class subdivision. To solve this problem, we created a shell program which, by combining the shell commands, the GIS Geographical Research Analysis Support System (GRASS) commands and the gawk language commands, carries out the whole procedure automatically. This makes the construction of a Landslide Susceptibility Map easy and fast for large areas too, and even when a high spatial resolution is adopted, as shown by application of the procedure to the Parma River basin, in the Italian Northern Apennines.     

基于信息量与逻辑回归模型的次生滑坡灾害敏感性评价——以汶川县北部为例

次生滑坡灾害的影响是震后较长时间里人们持续关注的焦点,对其开展敏感性评价具有重要意义。选取5.12地震的重灾区汶川县北部作为研究区,利用遥感与地理信息技术提取地震滑坡信息,在全面分析滑坡与高程、坡度、坡向、岩性、断裂带、地震烈度以及水系等7个影响因子相关特性的基础上,采用信息量法与逻辑回归模型进行灾害敏感性评价,将研究区划分为极轻度、轻度、中度、高度和极高危险5个级别,并对不同模型的适用性开展分析和对比。结果表明,逻辑回归模型在描述区域滑坡灾害危险度总体特征方面稍具优势。     

Qualitative landslide susceptibility assessment by multicriteria analysis: A case study from San Antonio del Sur, Guantánamo, Cuba

Geomorphological information can be combined with decision-support tools to assess landslide hazard and risk. A heuristic model was applied to a rural municipality in eastern Cuba. The study is based on a terrain mapping units (TMU) map, generated at 1:50,000 scale by interpretation of aerial photos, satellite images and field data. Information describing 603 terrain units was collected in a database. Landslide areas were mapped in detail to classify the different failure types and parts. Three major landslide regions are recognized in the study area: coastal hills with rockfalls, shallow debris flows and old rotational rockslides denudational slopes in limestone, with very large deep-seated rockslides related to tectonic activity and the Sierra de Caujerí scarp, with large rockslides. The Caujerí scarp presents the highest hazard, with recent landslides and various signs of active processes. The different landforms and the causative factors for landslides were analyzed and used to develop the heuristic model. The model is based on weights assigned by expert judgment and organized in a number of components such as slope angle, internal relief, slope shape, geological formation, active faults, distance to drainage, distance to springs, geomorphological subunits and existing landslide zones. From these variables a hierarchical heuristic model was applied in which three levels of weights were designed for classes, variables, and criteria. The model combines all weights into a single hazard value for each pixel of the landslide hazard map. The hazard map was then divided by two scales, one with three classes for disaster managers and one with 10 detailed hazard classes for technical staff. The range of weight values and the number of existing landslides is registered for each class. The resulting increasing landslide density with higher hazard classes indicates that the output map is reliable. The landslide hazard map was used in combination with existing information on buildings and infrastructure to prepare a qualitative risk map. The complete lack of historical landslide information and geotechnical data precludes the development of quantitative deterministic or probabilistic models.     

基于GIS的滑坡危险性逻辑回归评价研究

该文针对地质灾害研究中的核心问题——灾害危险性评价,以万州滑坡地质灾害为例,将滑坡风险评价中的各种因子归一化处理后转换成相同分辨率的定量数据,根据特定模型进行运算,得到风险评价图,利用逻辑回归分析法,进行滑坡地质灾害危险性评价。以解决过去地质灾害危险性评价中效率低、精度差、费时费力等问题,实现滑坡地质灾害的信息化、科学化。     

Landslide hazard in the Nebrodi Mountains (Northeastern Sicily)

The eastern sector of the Nebrodi Mountains (NE Sicily), a part of the Apenninic-Maghrebian orogenic chain, is characterized by an high landslide hazard. The village of S. Domenica Vittoria, which lies in the area, has been particularly affected by various landslide phenomena, with resulting damage to buildings and infrastructure.The rocks outcropping in the area belong to the Cretaceous Monte Soro Flysch; they consist of an alternation of argillaceous and calcareous beds at the base and argillaceous and quartzarenitic beds at the top. The lithotechnical characteristics of the formation and the steepness of the slopes in the area lead to an elevated instability, as testified by the widespread occurrence of sub-vertical arcuate cliffs (landslide scarps) and sub-horizontal areas (landslide terraces), typical of a landslide-controlled morphology. From a kinematics point of view, the observed phenomena can be referred to multiple rotational slides, flows, and complex landslides, often with a retrogressive development and enlargement. Triggering causes lie principally in the intense rainfalls that determine the decay of the geomechanical properties of the terrain and supply discontinuos groundwater circulation that is evident in seasonal springs. Human activity, such as the construction of roads and buildings on steep slopes and dispersal of water from supply systems and sewers has a significant impact as well.Due to the instability of the area, expansion of the village, which is already limited by the morphological conditions, is made difficult by the high hazard level, especially in the areas at higher elevations, where the principal landslide scarps are located, and even more on the rims of the scarps. Considering the high hazard level, S. Domenica Vittoria has been inserted by the National Geological Service among the sites in Sicily to be monitored by means of a GPS network. The survey carried out along the entire slope hosting the village has furnished the base for geological and geomorphological knowledge needed for the planning of the network, to identify the areas at landslide risk, where parts of the village lie, including the areas of expansion of the village, the main roads, and a portion of the Favoscuro river bed.     

An uncertainty and sensitivity analysis approach for GIS-based multicriteria landslide susceptibility mapping

GIS-based multicriteria decision analysis (MCDA) methods are increasingly being used in landslide susceptibility mapping. However, the uncertainties that are associated with MCDA techniques may significantly impact the results. This may sometimes lead to inaccurate outcomes and undesirable consequences. This article introduces a new GIS-based MCDA approach. We illustrate the consequences of applying different MCDA methods within a decision-making process through uncertainty analysis. Three GIS-MCDA methods in conjunction with Monte Carlo simulation (MCS) and Dempster–Shafer theory are analyzed for landslide susceptibility mapping (LSM) in the Urmia lake basin in Iran, which is highly susceptible to landslide hazards. The methodology comprises three stages. First, the LSM criteria are ranked and a sensitivity analysis is implemented to simulate error propagation based on the MCS. The resulting weights are expressed through probability density functions. Accordingly, within the second stage, three MCDA methods, namely analytical hierarchy process (AHP), weighted linear combination (WLC) and ordered weighted average (OWA), are used to produce the landslide susceptibility maps. In the third stage, accuracy assessments are carried out and the uncertainties of the different results are measured. We compare the accuracies of the three MCDA methods based on (1) the Dempster–Shafer theory and (2) a validation of the results using an inventory of known landslides and their respective coverage based on object-based image analysis of IRS-ID satellite images. The results of this study reveal that through the integration of GIS and MCDA models, it is possible to identify strategies for choosing an appropriate method for LSM. Furthermore, our findings indicate that the integration of MCDA and MCS can significantly improve the accuracy of the results. In LSM, the AHP method performed best, while the OWA reveals better performance in the reliability assessment. The WLC operation yielded poor results.     

A partial susceptibility approach to analysing the magnetic properties of environmental materials: a case study

An approach to expressing the magnetic properties of environmental materials in terms of the contributions of the magnetic susceptibilities of specific magnetic components is reported. The approach links the partial susceptibilities of discrete particles, domains or mineral fractions with the concentration-dependent parameters by means of multiple linear regression methods. The case study, using the Liverpool street dust data set, demonstrates that the technique is able to model the contributions of the main magnetic components satisfactorily. Several factors may have a significant impact on the regression results. These include the validity of the linear proportional relationships between partial susceptibilities and the relevant concentration-dependent parameters, the adequacy of the variable selection procedure and the regression model, and the suitability of certain magnetic parameters.     

引发意大利托斯卡纳区滑坡的区域降水阈值体系的定义——统计与环境分析(英文)

The aim of this work is the determination of regional-scale rainfall thresholds for the triggering of landslides in the Tuscany Region(Italy).The critical rainfall events related to the occurrence of 593 past landslides were characterized in terms of duration(D) and intensity(I).I and D values were plotted in a log-log diagram and a lower boundary was clearly noticeable:it was interpreted as a threshold representing the rainfall conditions associated to landsliding.That was also confirmed by a comparison with many literature thresholds,but at the same time it was clear that a similar threshold would be affected by a too large approximation to be effectively used for a regional warning system.Therefore,further analyses were performed differentiating the events on the basis of seasonality,magnitude,location,land use and lithology.None of these criteria led to discriminate among all the events different groups to be characterized by a specific and more effective threshold.This outcome could be interpreted as the demonstration that at regional scale the best results are obtained by the simplest ap-proach,in our case an empirical black box model which accounts only for two rainfall pa-rameters(I and D).So a set of thresholds could be conveniently defined using a statistical approach:four thresholds corresponding to four severity levels were defined by means of the prediction interval technique and we developed a prototype warning system based on rainfall recordings or weather forecasts.     

基于GIS的土壤全氮空间分布估算——以江西省兴国县为例

运用GIS的空间分析技术和DEM,在区域范围内,可以表征基于母岩和地形因子的土壤-景观模型。本次研究根据兴国县151个样点数据,分析TN和地形因子的相关关系,建立回归模型,进行估算。结果表明:表层土壤中TN含量平均值为1.06g/kg,千枚岩发育的土壤中TN的平均含量最高,为1.35g/kg ;砂页岩发育的土壤中TN的平均含量最低,为0.88g/kg 。空间分布上:TN含量在0.5g/kg~1.0g/kg的面积最大,为1580km²,TN含量在2.0g/kg以上的面积最小,为127km²。地形变量中坡向对TN含量影响最大,TN含量和母岩、海拔、坡向存在着正相关关系,坡度和TN含量的相关关系不明显。利用回归分析模型和DEM(30m×30m),估算TN的空间分布,R²为0.637。