Spatially and temporally distributed modeling of landslide susceptibility

Mapping of landslide susceptibility in forested watersheds is important for management decisions. In forested watersheds, especially in mountainous areas, the spatial distribution of relevant parameters for landslide prediction is often unavailable. This paper presents a GIS-based modeling approach that includes representation of the uncertainty and variability inherent in parameters. In this approach, grid-based tools are used to integrate the Soil Moisture Routing (SMR) model and infinite slope model with probabilistic analysis. The SMR model is a daily water balance model that simulates the hydrology of forested watersheds by combining climate data, a digital elevation model, soil, and land use data. The infinite slope model is used for slope stability analysis and determining the factor of safety for a slope. Monte Carlo simulation is used to incorporate the variability of input parameters and account for uncertainties associated with the evaluation of landslide susceptibility. This integrated approach of dynamic slope stability analysis was applied to the 72-km² Pete King watershed located in the Clearwater National Forest in north-central Idaho, USA, where landslides have occurred. A 30-year simulation was performed beginning with the existing vegetation covers that represented the watershed during the landslide year. Comparison of the GIS-based approach with existing models (FSmet and SHALSTAB) showed better precision of landslides based on the ratio of correctly identified landslides to susceptible areas. Analysis of landslide susceptibility showed that (1) the proportion of susceptible and non-susceptible cells changes spatially and temporally, (2) changed cells were a function of effective precipitation and soil storage amount, and (3) cell stability increased over time especially for clear-cut areas as root strength increased and vegetation transitioned to regenerated forest. Our modeling results showed that landslide susceptibility is strongly influenced by natural processes and human activities in space and time; while results from simulated outputs show the potential for decision-making in effective forest planning by using various management scenarios and controlling factors that influence landslide susceptibility. Such a process-based tool could be used to deal with real-dynamic systems to help decision-makers to answer complex landslide susceptibility questions.     

A landslide susceptibility model using the Analytical Hierarchy Process method and multivariate statistics in perialpine Slovenia

Landslides cause damage to property and unfortunately pose a threat even to human lives. Good landslide susceptibility, hazard, and risk models could help mitigate or even avoid the unwanted consequences resulted from such hillslope mass movements. For the purpose of landslide susceptibility assessment the study area in the central Slovenia was divided to 78 365 slope units, for which 24 statistical variables were calculated. For the land-use and vegetation data, multi-spectral high-resolution images were merged using Principal Component Analysis method and classified with an unsupervised classification. Using multivariate statistical analysis (factor analysis), the interactions between factors and landslide distribution were tested, and the importance of individual factors for landslide occurrence was defined. The results show that the slope, the lithology, the terrain roughness, and the cover type play important roles in landslide susceptibility. The importance of other spatial factors varies depending on the landslide type. Based on the statistical results several landslide susceptibility models were developed using the Analytical Hierarchy Process method. These models gave very different results, with a prediction error ranging from 4.3% to 73%. As a final result of the research, the weights of important spatial factors from the best models were derived with the AHP method. Using probability measures, potentially hazardous areas were located in relation to population and road distribution, and hazard classes were assessed.     

Landslide susceptibility zonation in the Rio Mendoza Valley, Argentina

A qualitative landslide susceptibility zonation map (scale 1:100,000) of a sector of the Río Mendoza Valley was prepared by overlapping thematic maps of conditioning factors. The main parameters affecting the distribution of landslides in the area were ranked according to slope instability. Geomorphological surveys and field observations enabled identification of 300 historical or prehistoric landslides. A landslide inventory was developed classifying the processes involved in slope instability, and analysing the degree of activity. The two most influential factors in decreasing slope stability were lithology and slope angle.     

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.     

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.     

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…     

Saline-alkali land in the Yellow River Delta: amelioration zonation based on GIS

Globally, an estimated land area of 9.55×108 ha is affected by salinity and sodicity[1]. In the Yellow River Delta, saline-alkali land covers more than 70% of the total area. Soil salinization is the key factor that influences sustainable agricultural development. Geographic information system (GIS) is a powerful tool for spatial data analysis, which can be used to analyze data from different sources for saline-alkali land monitoring. Based on GIS, zonation of saline-alkali land can provi…     

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.     

基于GIS的山洪灾害风险区划

1 Introduction Torrent is sudden surface runoff with sharp rises or falls (Tang, 1994; Zhao, 1996), resulting from rainfall in small basins of mountainous areas. It has features of violent coming force, rapid formation and strong destructivity which rende…     

Torrent risk zonation in the Upstream Red River Basin based on GIS

This paper explores the methodology for compiling the torrent hazard and risk zonation map by means of GIS technique for the Red River Basin in Yunnan province of China, where is prone to torrent. Based on a 1:250,000 scale digital map, six factors including slope angle, rainstorm days, buffer of river channels, maximum runoff discharge of standard area, debris flow distribution density and flood disaster history were analyzed and superimposed to create the torrent risk evaluation map. Population density, farmland percentage, house property, and GDP as indexes accounting for torrent hazards were analyzed in terms of vulnerability mapping. Torrent risk zonation by means of GIS was overlaid on the two data layers of hazard and vulnerability. Then each grid unit with a resolution of 500 m × 500 m was divided into four categories of the risk: extremely high, high, moderate and low. Finally the same level risk was combined into a confirmed zone, which represents torrent risk of the study area. The risk evaluation result in the upper Red River Basin shows that the extremely high risk area of 13,150 km2 takes up 17.9% of the total inundated area, the high risk area of 33,783 km2 is 45.9%, the moderate risk area of 18,563 km2 is 25.2% and the low risk area of 8115 km2 is 11.0%.     

Validation of a region-wide model of landslide susceptibility in the Manawatu–Wanganui region of New Zealand

Since European settlement 160 years ago, much of the indigenous forest in New Zealand hill country has been cleared for pastoral agriculture, resulting in increased erosion and sedimentation. To prioritise soil conservation work in the Manawatu–Wanganui region, we developed a model of landslide susceptibility. It assigns high susceptibility to steep land not protected by woody vegetation and low susceptibility everywhere else, following the commonly used approach for identifying inappropriate land use. A major storm on 15–16 February 2004 that produced many landslides was used to validate the model. The model predicted hills at risk to landsliding with moderate accuracy: 58% of erosion scars in the February storm occurred on hillsides considered to be susceptible. The model concept of slope thresholds, above which the probability of landsliding is high and below which the probability is low, is not adequate because below 30° the probability of landsliding is approximately linearly related to slope. Thus, reforestation of steep slopes will need to be combined with improved vegetation management for soil conservation on moderate slopes to significantly reduce future landsliding.     

Determining landslide susceptibility in Central Taiwan from rainfall and six site factors using the analytical hierarchy process method

This work provides a landslide susceptibility assessment model for rainfall-induced landslides in Central Taiwan based on the analytical hierarchy process method. The model considers rainfall and six site factors, including slope, geology, vegetation, soil moisture, road development and historical landslides. The rainfall factor consists of 10-day antecedent rainfall and total rainfall during a rainfall event. Landslide susceptibility values are calculated for both before and after the beginning of a rainfall event. The 175 landslide cases with detailed field surveys are used to determine a landslide-susceptibility threshold value of 9.0. When a landslide susceptibility assessment value exceeds the threshold value, slope failure is likely to occur. Three zones with different landslide susceptibility levels (below, slightly above, and far above the threshold) are identified. The 9149 landslides caused by Typhoon Toraji in Central Taiwan are utilized to validate the study's result. Approximately, 0.2%, 0.4% and 15.3% of the typhoon-caused landslides are located in the three landslide susceptibility zones, respectively. Three villages with 6.6%, 0.4% and 4.9% of the landslides respectively are used to validate the accuracy of the landslide susceptibility map and analyze the main causes of landslides. The landslide susceptibility assessment model can be used to evaluate susceptibility relative to accumulated rainfall, and is useful as an early warning and landslide monitoring tool.     

GIS techniques for regional-scale landslide susceptibility assessment: the Sicily (Italy) case study

This study describes the assessment of landslide susceptibility in Sicily (Italy) at a 1:100,000 scale using a multivariate logistic regression model. The model was implemented in a GIS environment by using the ArcSDM (Arc Spatial Data Modeller) module, modified to develop spatial prediction through regional data sets. A newly developed algorithm was used to automatically extract the detachment area from mapped landslide polygons. The following factors were selected as independent variables of the logistic regression model: slope gradient, lithology, land cover, a curve number derived index and a pluviometric anomaly index. The above-described configuration has been verified to be the best one among others employing from three to eight factors. All the regression coefficients and parameters were calculated using selected landslide training data sets. The results of the analysis were validated using an independent landslide data set. On an average, 82% of the area affected by instability and 79% of the not affected area were correctly classified by the model, which proved to be a useful tool for planners and decision-makers.     

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.     

Mapping landslide susceptibility from small datasets: A case study in the Pays de Herve (E Belgium)

A landslide susceptibility map is proposed for the Pays de Herve (E Belgium), where large landslides affect Cretaceous clay outcrop areas. Based on a Bayesian approach, this GIS-supported probabilistic map identifies the areas most susceptible to deep landslides. The database is comprised of the source areas of ten pre-existing landslides (i.e. a sample of 154 grid cells) and of six environmental data layers, namely lithology, proximity to active faults, slope angle and aspect, elevation and distance to the nearest valley-floor. A 30-m-resolution DEM from the Belgian National Geographical Institute is used for the analysis. Owing to the small size of the sample, a special cross-validation procedure of the susceptibility map is performed, which uses in an iterative way each of the landslides to test the predictive power of the map derived from the other landslides. Four different sets of variables are used to produce four susceptibility maps, whose prediction curves are compared. While the prediction rates associated with the models not involving the “proximity to active fault” criterion are comparable to those of the models considering this variable, strong weaknesses inherent in the fault data on which the latter rely suggest that the final susceptibility map should be based on a model that excludes any reference to fault. This highlights the difference between a triggering factor and determining factors, and in the same time broadens the scope of the produced map. A single reactivated slide is also used to test the possibility of predicting future reactivation of existing landslides in the area. Finally, the need for geomorphological control over the mathematical treatment is underlined in order to obtain realistic prediction maps.     

Landslide susceptibility mapping by correlation between topography and geological structure: the Janghung area, Korea

The purpose of this study is to develop and apply the technique for landslide susceptibility analysis using geological structure in a Geographic Information System (GIS). In the study area, the Janghung area of Korea, landslide locations were detected from Indian Remote Sensing (IRS) satellite images by change detection, where the geological structure of foliation was surveyed and analysed. The landslide occurrence factors (location of landslide, geological structure and topography) were constructed into a spatial database. Then, strike and dip of the foliation and the aspect and slope of the topography were compared and the results, which were verified using landslide location data, show that foliation of gneiss has a geometrical relation to the joint or fault that leads to a landslide. Using the geometrical relations, the landslide susceptibility was assessed and verified. The verification results showed satisfactory agreement between the susceptibility map and the landslide location data.     

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.     

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.