Deterministic Landslide Risk Assessment at a Past Landslide Site

The slope failure and landslide hazard will possess the same properties within a range including the same engineering geological conditions. To assess the landslide risk of a mountainous area, the study of landslides previously having occurred is very important to evaluate the landslide risk around the area in which they took place. Based on the study of the mechanism of a previous landslide recorded in Kumamoto, Japan, this study initially proposes mechanical parameters for evaluating the landslide hazard using a 3D slope stability method. For each slope unit in the study area, the critical slip surface, which reveals the minimum safety factor of a slope, can be obtained. The affected streams and range of possible landslide masses are analyzed based on the debris flow simulation. This is initially applied to simulate the past landslide event and the result shows the landslide-deduced debris flow effectively re-displayed. Overlayered with layers of infrastructure in Geographic Information Systems (GIS), this risk map indicates which houses and road sections remain in dangerous areas.     

Landslide hazard mapping along national highway-154A in Himachal Pradesh,India using information value and frequency ratio

For the socio-economic development of a country, the highway network plays a pivotal role. It has therefore become an imperative to have landslide hazard assessment along these roads to provide safety. The current study presents landslide hazard zonation maps, based on the information value method and frequency ratio method using GIS on 1:50,000 scale by generating the information about the landslide influencing factors. The study was carried out in the year 2017 on a part of Ravi river catchment along one of the landslide-prone Chamba to Bharmour road corridor of NH-154A in Himachal Pradesh, India. A number of landslide triggering geo-environmental factors like “slope, aspect, relative relief, soil, curvature, Land Use and Land Cover (LULC), lithology, drainage density, and lineament density” were selected for landslide hazard mapping based on landslide inventory. The landslide inventory has been developed using satellite imagery, Google earth and by doing exhaustive field surveys. A digital elevation model was used to generate slope gradient, slope aspect, curvature, and relative relief map of the study area. The other information, i.e., soil maps, geological maps, and toposheets, have been collected from various departments. The landslide hazard zonation map was categorized namely “very high hazard, high hazard, medium hazard, low hazard, and very low hazard.” The results from these two methods have been validated using area under curve (AUC) method. It has been found that hazard zonation map prepared using frequency ratio model had a prediction rate of 75.37% while map prepared using information value method had prediction rate of 78.87%. Hence, on the basis of prediction rate, the landslide hazard zonation map, obtained using information value method, was experienced to be more suitable for the study area.     

Landslide activity as a geoindicator in Italy: significance and new perspectives from remote sensing

Landslide activity responds to rapid environmental changes and represents a relevant geoindicator in mountainous or hilly areas. This paper discusses the socio-economic relevance of landslide hazard in Italy and the problems encountered in establishing relationships between landslide frequency, climate and vegetation changes at different time scales. Landslides blocking a river channel have been carefully taken into account because they are usually characterized by high intensities (with regard to the involved masses and movement velocities) and their occurrences are often datable via radiocarbon dating. This is due to the recovery of organic matter in the landslide dammed lakes. For these reasons they can be considered important geoindicators in the wider category of slope failures. The marked effects of the anthropogenic activity on slope instability processes in the last 50 years are discussed with reference to two case histories: the Chianti hills in Tuscany and the Cinque Terre National Park in Liguria. Finally, two novel techniques of remote sensing are proposed as tools for a systematic monitoring of slope instability at different time and spatial scales. Both techniques are based on the interferometric synthetic aperture radar (SAR) technology and differ on the type of platform (satellite and ground-based) used to acquire data.     

Regional Assessment of the Effects of Land-Use Change on Landslide Hazard By Means of Physically Based Modelling

Physically based models are capable of evaluating the effects of environmental changes through adaptations in their parameters. For landslide hazard zonation, this gives them an edge over traditional, statistically based techniques that require large datasets and often lack the objectivity to achieve the same purpose. Therefore, physical models can be valuable tools for hazard assessment and planning purposes.The usefulness of the model prognosis depends largely on the ability of the physical model to mimic the landscape system. This implies that the model should be calibrated and validated and that the imposed changes do not lead to a radical departure from the present situation.Under the recognition of these constraints, a physically based model has been applied to a 1.5 km² catchment in the Alcoy region (SE Spain) to evaluate the effects of land use change on landslide activity. The model couples a transient, distributed hydrological model with a probabilistic assessment of the slope stability. Thus, it is able to assess the spatial and temporal activity of slope instability. For the present situation, validation demonstrates that the probability of failure returns a conservative estimate of the spatial frequency of landsliding. The model has subsequently been applied to two hypothetical land use change scenarios that extrapolate present and likely trends. For these scenarios, the model results indicate a marginal decrease in the spatial frequency of landsliding (aerial extent of instability). However, the decrease in the temporal activity (is total duration of instability over a given period) is substantial under the altered land use conditions. The forecasted change in landslide activity not only affects the relative weight of slope processes in the region. It also has implications for the perceived hazard levels and the landslide hazard zonation of the area.     

Landslide hazard evaluation and zonation mapping in mountainous terrain

Landslide hazard zonation (LHZ) maps are of great help to planners and field engineers for selecting suitable locations to implement development schemes in mountainous terrain, as well as, for adopting appropriate mitigation measures in unstable hazard-prone areas. A new quantitative approach has been evolved, based on major causative factors of slope instability. A case study of landslide hazard zonation in the Himalaya, adopting a landslide- hazard evaluation factor (LHEF) rating scheme, has been presented.     

Neural networks and landslide susceptibility: a case study of the urban area of Potenza

For those working in the field of landslide prevention, the estimation of hazard levels and the consequent production of thematic maps are principal objectives. They are achieved through careful analytical studies of the characteristics of landslide prone areas, thus, providing useful information regarding possible future phenomena. Such maps represent a fundamental step in the drawing up of adequate measures of landslide hazard mitigation. However, for a complete estimation of landslide hazard, meant as the degree of probability that a landslide occurs in a given area, within a given space of time, detailed and uniformly distributed data regarding their incidence and causes are required. This information, while obtainable through laborious historical research, is usually partial, incomplete and uneven, and hence, unsatisfactory for zoning on a regional scale. In order to carry this out effectively, the utilization of spatial estimation of the relative levels of landslide hazard in the various areas was considered opportune. These areas were classified according to their levels of proneness to landslide activity without taking recurrence periods into account. Various techniques were developed in order to obtain upheaval numerical estimates. The method used in this study, which was applied in the area of Potenza, is based on techniques derived from artificial intelligence (Artificial Neural Network—ANN). This method requires the definition of appropriate thematic layers, which parameterize the area under study. These are recognized by means of specific analyses in a functional relationship to the event itself. The parameters adopted are: slope gradient, slope aspect, topographical index, topographical shape, elevation, land use and lithology.     

Landslide Susceptibility Mapping Using Fuzzy Logic System and Its Influences on Mainlines in Lashgarak Region,Tehran, Iran

Landslide susceptibility mapping is among the useful tools applied in disaster management and planning development activities in mountainous areas. The susceptibility maps prepared in this research provide valuable information for landslide hazard management in Lashgarak region of Tehran. This study was conducted to, first, prepare landslide susceptibility maps for Lashgarak region and evaluate landslide effect on mainlines and, second, to analyze the main factors affecting landslide hazard increase in the study area in order to propose efficient strategies for landslide hazard mitigation. A GIS-based multi-criteria decision analysis model (fuzzy logic) is used in the present work for scientific evaluation of landslide susceptible areas in Lashgarak region. To this end, ArcGIS, PCIGeomatica, and IDIRISI software packages were used. Eight information layers were selected for information analysis: ground strength class, slope angle, terrain roughness, normalized difference moisture index, normalized difference vegetation index, distance from fault, distance from the river, and distance from the road. Next, eight different scenarios were created to determine landslide susceptibility of the study area using different operators (intersection (AND), union (OR), algebraic sum (SUM), multiplication (PRODUCT), and different fuzzy gamma values) of fuzzy overlay approach. After that, the performance of various fuzzy operators in landslide susceptibility mapping was empirically compared. The results revealed the excellent consistency of landslide susceptibility map prepared using the fuzzy union (OR) operator with landslide distribution map in the study area. Eventually, the accuracy of landslide susceptibility map prepared using the fuzzy union (OR) operator was evaluated using the frequency ratio diagram. The results showed that frequency values of the landslides gradually increase from “low susceptibility” to high “susceptibility” as 88.34% of the landslides are categorized into two “high” and “very high” susceptibility classes, implying the satisfactory consistency between the landslide susceptibility map prepared using fuzzy union (OR) operator and landslide distribution map.     

Landslide vulnerability and risk assessment for multi-hazard scenarios using airborne laser scanning data (LiDAR)

Landslide hazard, vulnerability, and risk-zoning maps are considered in the decision-making process that involves land use/land cover (LULC) planning in disaster-prone areas. The accuracy of these analyses is directly related to the quality of spatial data needed and methods employed to obtain such data. In this study, we produced a landslide inventory map that depicts 164 landslide locations using high-resolution airborne laser scanning data. The landslide inventory data were randomly divided into a training dataset: 70 % for training the models and 30 % for validation. In the initial step, a susceptibility map was developed using logistic regression approach in which weights were assigned to every conditioning factor. A high-resolution airborne laser scanning data (LiDAR) was used to derive the landslide conditioning factors for the spatial prediction of landslide hazard areas. The resultant susceptibility was validated using the area under the curve method. The validation result showed 86.22 and 84.87 % success and prediction rates, respectively. In the second stage, a landslide hazard map was produced using precipitation data for 15 years. The precipitation maps were subsequently prepared and show two main categories (two temporal probabilities) for the study area (the average for any day in a year and abnormal intensity recorded in any day for 15 years) and three return periods (15-, 10-, and 5-year periods). Hazard assessment was performed for the entire study area. In the third step, an element at risk map was prepared using LULC, which was considered in the vulnerability assessment. A vulnerability map was derived according to the following criteria: cost, time required for reconstruction, relative risk of landslide, risk to population, and general effect to certain damage. These criteria were applied only on the LULC of the study area because of lack of data on the population and building footprint and types. Finally, risk maps were produced using the derived vulnerability and hazard information. Thereafter, a risk analysis was conducted. The LULC map was cross-matched with the results of the hazard maps for the return period, and the losses were aggregated for the LULC. Then, the losses were calculated for the three return periods. The map of the risk areas may assist planners in overall landslide hazard management.     

Application of sky view factor technique to the interpretation and reactivation assessment of landslide activity

High-resolution digital elevation models are crucial to the investigation of natural disasters, and a variety of methods based on visualization and relief map compilations have been proposed. In this study, the sky view factor (SVF) is applied to slope maps and a digital elevation model (DEM) of the Oso landslide, a deadly landslide that occurred in Washington State on March 22, 2014, to demonstrate the effectiveness of SVF-enhanced relief maps in mapping and evaluating large-scale or deep-seated landslide hazards. A procedure for combining the SVF-enhanced DEM with slope and elevation maps is also presented. Then the maps are used to extract the landslide-prone areas and perform a reactivation analysis of the post-Oso landslide using an analytic hierarchy process (AHP). By using the SVF-enhanced DEM to perform the AHP assessment on multi-period images, we accurately evaluate hazard of the landslide for both pre and post-2014 conditions. Finally, different visualization maps, limitation and recommend parameters for generating SVF relief map are presented in the paper.     

Influence of seismic acceleration on landslide susceptibility maps: a case study from NE Turkey (the Kelkit Valley)

Particularly in the last decade, landslide susceptibility and hazard maps have been used for urban planning and site selection of infrastructures. Most of the procedures for preparing of landslide susceptibility maps need high-quality landslide inventory map. Although the rainfall and seismic activities are accepted as triggering factor for landslides, designation of the triggering factor for each landslide in the inventory is almost impossible when well-documented records are unavailable. Therefore, during preparation of landslide susceptibility map, whole landslide records in the inventory map are used together without classifying based on the triggering factors. Although seismic activity is accepted as a triggering factor, possible effect of the use of seismic activity on production of landslide susceptibility map was investigated in this study, and the subject is open to discussion. For this purpose, a series of stability analyses based on circular failure and infinite slope model were performed considering different pseudostatic conditions. The results of analyses show that gentle slopes have higher susceptibility to failure than steeper ones, even if their stability conditions (susceptibilities) are similar for static condition. The seismic forces acting on failure surfaces may not be sufficiently taken into consideration in the conventionally prepared landslide susceptibility maps. Employing the general decreasing trend in stability condition based on slope face angle and the seismic acceleration, a new procedure was introduced for preparing of the landslide susceptibility map for a scenario earthquake. The prediction performance of occurring landslides increased after the procedure was applied to the conventionally prepared landslide susceptibility map. According to the threshold independent spatial performance analyses of the proposed methodology and the produced landslide susceptibility maps, the area under ROC curve values were calculated as 0.801, 0.933, and 0.947 for the maps prepared by considering conventional method and scenario earthquakes having M _w values of 5.5 and 7.5, respectively.     

Landslide susceptibility analysis in the Hoa Binh province of Vietnam using statistical index and logistic regression

The purpose of this study is to evaluate and compare the results of applying the statistical index and the logistic regression methods for estimating landslide susceptibility in the Hoa Binh province of Vietnam. In order to do this, first, a landslide inventory map was constructed mainly based on investigated landslide locations from three projects conducted over the last 10 years. In addition, some recent landslide locations were identified from SPOT satellite images, fieldwork, and literature. Secondly, ten influencing factors for landslide occurrence were utilized. The slope gradient map, the slope curvature map, and the slope aspect map were derived from a digital elevation model (DEM) with resolution 20 × 20 m. The DEM was generated from topographic maps at a scale of 1:25,000. The lithology map and the distance to faults map were extracted from Geological and Mineral Resources maps. The soil type and the land use maps were extracted from National Pedology maps and National Land Use Status maps, respectively. Distance to rivers and distance to roads were computed based on river and road networks from topographic maps. In addition, a rainfall map was included in the models. Actual landslide locations were used to verify and to compare the results of landslide susceptibility maps. The accuracy of the results was evaluated by ROC analysis. The area under the curve (AUC) for the statistical index model was 0.946 and for the logistic regression model, 0.950, indicating an almost equal predicting capacity.     

Integrated Landslide Susceptibility Analysis and Hazard Assessment in the Principality of Andorra

In the paper we present the procedure for hazard assessment that has been used to prepare the landslide hazard map of the Principality of Andorra at 1:5,000 scale. The main phases of the hazard assessment are discussed. Susceptibility analysis has involved the location of the potential slope failures, and the estimation of both landslide volume and runout distance. In the susceptible areas, landslide magnitude and frequency has been determined in order to produce the Hazard Zoning Map. Data required for hazard assessment have been introduced into a GIS or derived directly from available Digital Terrain Models. Data handling and treatment with the GIS has allowed the performance of the landslide hazard assessment and mapping in a fast and reproducible way.     

The spatial correlation between earthquakes and landslides in Hokkaido (Japan), a GIS-based analysis of the past and the future

Although earthquakes are thought to be one of the factors responsible for the occurrence of landslides in Hokkaido, there exist no enough records which can allow correlating many of the old slope failures in the island with earthquakes. In the absence of these records, an attempt was done in this study to use the abundance, frequency, magnitude, depth, and distribution of historical earthquakes to deduce that many of the slope failures in the region were triggered by strong and continuous seismicity. The determination of the zones of influences of selected earthquakes using an existing empirical function has also supported this conclusion. Moreover, the use of a 10% probability of exceedance of earthquake intensity in 50 years, and the geological and slope maps has allowed preparing a landslide hazard map which explains the role of future earthquakes in the formation of slope failures. The result indicates a high probability of occurrences of landslides in the hilly regions of the southeastern part of Hokkaido due to expected strong seismicity and earthquake intensities in these areas. On the other hand, the low level of intensity in the north has given rise to low probability of landslide hazard. There are also places in the center of the island and high intensity regions in the east where the probability of landslide hazard was influenced by the contribution of the geological and slope maps.     

Landslide consequence analysis: a region-scale indicator-based methodology

Consequence analysis is, together with hazard evaluation, one of the major steps of landslide risk assessment. However, a significant discrepancy exists between the number of published landslide hazard and landslide consequence studies. While various methodologies for regional-scale hazard assessment have been developed during the last decade, studies for estimating and visualising possible landslide consequences are still limited, and those existing are often difficult to apply in practice mainly because of the lack of data on the historical damage or on landslide damage functions. In this paper, an indicator-based GIS-aided methodology is proposed with an application to regional-scale consequence analysis. The index, called Potential Damage Index, allows describing, quantifying, valuing, totalizing and visualising different types of consequences. The method allows estimating the possible damage caused by landslides by combining weighted indicators reflecting the exposure of the elements at risk. Direct (physical injury, and structural and functional damage) and indirect (socio-economic impacts) consequences are individually analysed and subsequently combined to obtain a map of total consequences due to landsliding. Geographic visualisation of the index allows the delineation of the areas exposed to any type of possible impacts that could be combined with a corresponding map displaying landslide probability of occurrence. The method has been successfully applied to analyse the present consequences in the Barcelonnette Basin (South French Alps). These maps contribute to development of adequate land use and evacuation plans, and thus are important tools for local authorities and insurance companies.     

Landslide hazard and risk mapping at catchment scale in the Arno River basin

We present the methodologies adopted and the outcomes obtained in the analysis of landslide risk in the basin of the Arno River (Central Italy) in the framework of a project sponsored by the Basin Authority of the Arno River, started in the year 2002 and completed at the beginning of 2005. In particular, a complete set of methods and applications for the assessment of landslide susceptibility and risk are described and discussed. A new landslide inventory of the whole area was realized, using conventional (aerial-photo interpretation and field surveys) and non-conventional methods (e.g. remote sensing techniques such as DInSAR and PS-InSAR). The great majority of the mapped mass movements are rotational slides (75%), solifluctions and other shallow slow movements (17%) and flows (5%), while soil slips, and other rapid landslides, seem less frequent everywhere within the basin. The relationships between landslide characteristics and environmental factors have been assessed through statistical analysis. As expected, the results show a strong control of land cover, lithology and morphology on landslide occurrence. The landslide frequency-size distribution shows a typical scaling behaviour already underlined in other landslide inventories worldwide. The assessment of landslide hazard in terms of probability of occurrence in a given time, based for mapped landslides on direct and indirect observations of the state of activity and recurrence time, has been extended to landslide-free areas through the application of statistical methods implemented in an artificial neural network (ANN). Unique conditions units (UCU) were defined by the map overlay of landslide preparatory factors (lithology, land cover, slope gradient, slope curvature and upslope contributing area) and afterwards used to construct a series of model vectors for the training and test of the ANN. Various different ANNs were selected throughout the basin, until each UCU was assigned a degree of membership to a susceptibility and a hazard class. Model validation confirms that prediction results are very good, with an average percentage of correctly recognized mass movements of about 85%. The analysis also revealed the existence of a large number of unmapped mass movements, thus contributing to the completeness of the final inventory. Temporal hazard was estimated via the translation of state of activity in recurrence time and hence probability of occurrence. The following intersection of hazard values with vulnerability and exposure figures, obtained by reclassification of digital vector mapping at 1:10,000 scale, lead to the definition of risk values for each terrain unit for different periods of time into the future. The final results of the research are now undergoing a process of integration and implementation within land planning and risk prevention policies and practices at local and national level.     

Landslide temporal analysis and susceptibility assessment as bases for landslide mitigation, Machu Picchu, Peru

Multi-temporal landslide occurrence information acquired through aerial photo interpretation and field mapping was used to assess occurrence frequencies on the slopes around the UNESCO cultural world heritage site of Machu Picchu, Peru. This showed that the coarse time resolution of the historical landslide information may lead to inaccurate interpretations regarding landslide occurrence frequencies in some parts of the study area. In addition, the assumption that the past landslide frequency can be used to describe the future landslide occurrence was not proved in the study area. Thereafter, unique conditional analyses were undertaken to assess landslide susceptibility using a limited number of preparatory factor maps. It showed that large majority of the Inca City is located on least susceptible areas within the region. The results of the susceptibility assessment combined with landslide occurrence frequencies may serve as a basis for the landslide hazard mitigation in the studied area. For these purposes, pixel-based susceptibility maps were generalized into expert-defined landslide management units. These units provide site managers with easily understandable and applicable hence reliable information about future landslide occurrences. An approach describing usage of the resulting susceptibility maps for onsite mitigation purposes was described with respect to the needs of Machu Picchu site managers.     

GIS-Based Probabilistic Mapping of Landslide Hazard Using a Three-Dimensional Deterministic Model

Based on a new Geographic Information Systems (GIS) grid-basedthree-dimensional (3-D) deterministic model and taking the slopeunit as the mapping unit, this study maps landslide hazard usingthe 3-D safety factor index and failure probability. Assuming theinitial slip to be the lower part of an ellipsoid, the 3-D critical slipsurface in the 3-D slope stability analysis is located by minimizingthe 3-D safety factor using the Monte Carlo random simulation.The failure probability of the landslide is calculated using anapproximate method in which the distributions of c, andthe 3-D safety factor are assumed to be in normal distribution.The method has been applied to a case study on three-dimensionallyand probabilistically mapping landslide hazard.     

Landslide dam formation susceptibility analysis based on geomorphic features

Global climate change has increased the frequency of abnormally high rainfall; such high rainfall events in recent years have occurred in the mountainous areas of Taiwan. This study identifies historical earthquake- and typhoon-induced landslide dam formations in Taiwan along with the geomorphic characteristics of the landslides. Two separate groups of landslides are examined which are classified as those that were dammed by river water and those that were not. Our methodology applies spatial analysis using geographic information system (GIS) and models the geomorphic features with 20?×?20 m digital terrain mapping. The Spot 6 satellite images after Typhoon Morakot were used for an interpretation of the landslide areas. The multivariate statistical analysis is also used to find which major factors contribute to the formation of a landslide dam. The objective is to identify the possible locations of landslide dams by the geomorphic features of landslide-prone slopes. The selected nine geomorphic features include landslide area, slope, aspect, length, width, elevation change, runout distance, average landslide elevation, and river width. Our four geomorphic indexes include stream power, form factor, topographic wetness, and elevation–relief ratio. The features of the 28 river-damming landslides and of the 59 non-damming landslides are used for multivariate statistical analysis by Fisher discriminant analysis and logistic regression analysis. The principal component analysis screened out eleven major geomorphic features for landslide area, slope, aspect, elevation change, length, width, runout distance, average elevation, form factor, river width, stream power, and topography wetness. Results show that the correctness by Fisher discriminant analysis was 68.0 % and was 70.8 % by logistic regression analysis. This study suggests that using logistic regression analysis as the assessment model for identifying the potential location of a landslide dam is beneficial. Landslide threshold equations applying the geomorphic features of slope angle, angle of landslide elevation change, and river width (H _L/W _R) to identify the potential formation of natural dams are proposed for analysis. Disaster prevention and mitigation measures are enhanced when the locations of potential landslide dams are identified; further, in order to benefit such measures, dam volume estimates responsible for breaches are key.     

基于GIS的巴东新县城滑坡灾害风险系统

本文提出了基于GIS的滑坡灾害风险预测系统流程。并将滑坡灾害风险评价模型与GIS技术先进的图形处理和空间分析功能相结合,建立了巴东县新县城区滑坡灾害风险预测系统。系统由信息管理子系统、危险性预测子系统、易损性预测子系统、风险预测子系统四大子系统构成。系统在对相关信息进行采集、存贮、检索和管理的基础上,结合物元模型、BP模型等专业预测模型,实现了滑坡灾害危险性、易损性评价,最终取得了滑坡灾害风险分布图,为三峡库区内各县的滑坡灾害信息管理和风险预测提供了新途径。预测成果可为研究区的国土规划和移民工程的顺利实施提供依据和保障。