Assessment of shallow landslide susceptibility using the transient infiltration flow model and GIS-based probabilistic approach

This study proposes a probabilistic analysis method for modeling rainfall-induced shallow landslide susceptibility by combining a transient infiltration flow model and Monte Carlo simulations. The spatiotemporal change in pore water pressure over time caused by rainfall infiltration is one of the most important factors causing landslides. Therefore, the transient infiltration hydrogeological model was adopted to estimate the pore water pressure within the hill slope and to analyze landslide susceptibility. In addition, because of the inherent uncertainty and variability caused by complex geological conditions and the limited number of available soil samples over a large area, this study utilized probabilistic analysis based on Monte Carlo simulations to account for the variability in the input parameters. The analysis was performed in a geographic information system (GIS) environment because GIS can deal efficiently with a large volume of spatial data. To evaluate its effectiveness, the proposed analysis method was applied to a study area that had experienced a large number of landslides in July 2006. For the susceptibility analysis, a spatial database of input parameters and a landslide inventory map were constructed in a GIS environment. The results of the landslide susceptibility assessment were compared with the landslide inventory, and the proposed approach demonstrated good predictive performance. In addition, the probabilistic method exhibited better performance than the deterministic alternative. Thus, analysis methods that account for uncertainties in input parameters are more appropriate for analysis of an extensive area, for which uncertainties may significantly affect the predictions because of the large area and limited data.     

Statistical approach to earthquake-induced landslide susceptibility

Susceptibility analysis for predicting earthquake-induced landslides has most frequently been done using deterministic methods; multivariate statistical methods have not previously been applied. In this study, however, we introduce a statistical methodology that uses the intensity of earthquake shaking as a landslide triggering factor. This methodology is applied in a study of shallow earthquake-induced landslides in central western Taiwan. The results show that we can accurately interpret landslide distribution in the study area and predict the occurrence of landslides in neighboring regions. This susceptibility model is capable of predicting shallow landslides induced during an earthquake scenario with similar range of ground shaking, without requiring the use of geotechnical, groundwater or failure depth data.     

Cross-application used to validate landslide susceptibility maps using a probabilistic model from Korea

A study of landslides in Youngin, Janghung and Boeun, Korea, using the geographic information system (GIS) validates a spatial probabilistic model for landslide susceptibility analysis. Locations were identified from aerial photographs, satellite images and field surveys. Topography, soil-type, forest-cover and land-cover maps were constructed from spatial data sets. Landslide occurrence is influenced by 13 factors, evidence for which was extracted from the database with the frequency ratio of each factor computed. Landslide susceptibility maps use frequency ratios derived not only from data for each area but also ratios, one from the probabilistic model, calculated from the other two areas (nine maps in all) as a cross-check of method validity. For validation, analytical results were compared in each study area with actual landslide locations: Boeun based on its frequency ratio showed the best accuracy (82.49%) whereas Janghung based on the Boeun frequency ratio showed the worst (69.53%).     

Three-dimensional mapping of a landslide using a multi-geophysical approach: the Quesnel Forks landslide

A landslide located on the Quesnel River in British Columbia, Canada is used as a case study to demonstrate the utility of a multi-geophysical approach to subsurface mapping of unstable slopes. Ground penetrating radar (GPR), direct current (DC) resistivity and seismic reflection and refraction surveys were conducted over the landslide and adjacent terrain. Geophysical data were interpreted based on stratigraphic and geomorphologic observations, including the use of digital terrain models (DTMs), and then integrated into a 3-dimensional model. GPR surveys yielded high-resolution data that were correlated with stratigraphic units to a maximum depth of 25 m. DC electrical resistivity offered limited data on specific units but was effective for resolving stratigraphic relationships between units to a maximum depth of 40 m. Seismic surveys were primarily used to obtain unit boundaries up to a depth of >80 m. Surfaces of rupture and separation were successfully identified by GPR and DC electrical resistivity techniques.     

Modelling shallow landslide susceptibility: a new approach in logistic regression by using favourability assessment

A new method for estimating shallow landslide susceptibility by combining Geographical Information System (GIS), nonparametric kernel density estimation and logistic regression is described. Specifically, a logistic regression is applied to predict the spatial distribution by estimating the probability of occurrence of a landslide in a 16 km² area. For this purpose, a GIS is employed to gather the relevant sample information connected with the landslides. The advantages of pre-processing the explanatory variables by nonparametric density estimation (for continuous variables) and a reclassification (for categorical/discrete ones) are discussed. The pre-processing leads to new explanatory variables, namely, some functions which measure the favourability of occurrence of a landslide. The resulting model correctly classifies 98.55% of the inventaried landslides and 89.80% of the landscape surface without instabilities. New data about recent shallow landslides were collected in order to validate the model, and 92.20% of them are also correctly classified. The results support the methodology and the extrapolation of the model to the whole study area (278 km²) in order to obtain susceptibility maps.     

Application and verification of a fractal approach to landslide susceptibility mapping

Landslide susceptibility mapping is essential for land-use activities and management decision making in hilly or mountainous regions. The existing approaches to landslide susceptibility zoning and mapping require many different types of data. In this study, we propose a fractal method to map landslide susceptibility using historical landslide inventories only. The spatial distribution of landslides is generally not uniform, but instead clustered at many different scales. In the method, we measure the degree of spatial clustering of existing landslides in a region using a box-counting method and apply the derived fractal clustering relation to produce a landslide susceptibility map by means of GIS-supported spatial analysis. The method is illustrated by two examples at different regional scales using the landslides inventory data from Zhejiang Province, China, where the landslides are mainly triggered by rainfall. In the illustrative examples, the landslides from the inventory are divided into two time periods: The landslides in the first period are used to produce a landslide susceptibility map, and those in the late period are taken as validation samples for examining the predictive capability of the landslide susceptibility maps. These examples demonstrate that the landslide susceptibility map created by the proposed technique is reliable.     

A heuristic approach to global landslide susceptibility mapping

Landslides can have significant and pervasive impacts to life and property around the world. Several attempts have been made to predict the geographic distribution of landslide activity at continental and global scales. These efforts shared common traits such as resolution, modeling approach, and explanatory variables. The lessons learned from prior research have been applied to build a new global susceptibility map from existing and previously unavailable data. Data on slope, faults, geology, forest loss, and road networks were combined using a heuristic fuzzy approach. The map was evaluated with a Global Landslide Catalog developed at the National Aeronautics and Space Administration, as well as several local landslide inventories. Comparisons to similar susceptibility maps suggest that the subjective methods commonly used at this scale are, for the most part, reproducible. However, comparisons of landslide susceptibility across spatial scales must take into account the susceptibility of the local subset relative to the larger study area. The new global landslide susceptibility map is intended for use in disaster planning, situational awareness, and for incorporation into global decision support systems.     

Statistical modelling of Europe-wide landslide susceptibility using limited landslide inventory data

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

基于Mamdani FIS模型的滑坡易发性评价研究

滑坡的形成是众多非线性关系的影响因子相互作用的结果,传统滑坡预测方法需要大量实地勘查数据。利用Mamdani FIS(模糊推理系统)模型对三峡库区巴东-秭归段进行滑坡易发性预测,并对结果进行评价。通过地理信息系统(geographic information system,GIS)、遥感(remote sensing,RS)技术和区域地质背景资料获取地形类、生态环境类和地质背景类共3类7种滑坡影响因子,建立了192条相关的推理规则,在Matlab平台下基于Mamdani FIS模型得到研究区滑坡易发性预测指数,并生成滑坡易发性区划图。预测结果的受试者工作特征曲线下的面积值为82.8%,显示滑坡评估效果良好。结果证明,与其他模型相比,基于空间信息技术的Mamdani FIS模型,利用其非线性分析能力和基于专家意见的推理规则,评估滑坡易发性时不需要先验知识支撑,简化了模型使用时对数据的要求。另外,该模型只需通过专家意见改变推理规则就可以应用于不同的地质地理环境区域,显示其较强的适应性。     

Regional landslide susceptibility analysis using back-propagation neural network model at Cameron Highland,Malaysia

This paper presents landslide susceptibility analysis around the Cameron Highlands area, Malaysia using a geographic information system (GIS) and remote sensing techniques. Landslide locations were identified in the study area from interpretation of aerial photographs and field surveys. Topographical, geological data and satellite images were collected, processed, and constructed into a spatial database using GIS and image processing. Ten landslide occurrence factors were selected as: topographic slope, topographic aspect, topographic curvature and distance from drainage, lithology and distance from lineament, soil type, rainfall, land cover from SPOT 5 satellite images, and the vegetation index value from SPOT 5 satellite image. These factors were analyzed using an advanced artificial neural network model to generate the landslide susceptibility map. Each factor’s weight was determined by the back-propagation training method. Then, the landslide susceptibility indices were calculated using the trained back-propagation weights, and finally, the landslide susceptibility map was generated using GIS tools. The results of the neural network model suggest that the effect of topographic slope has the highest weight value (0.205) which has more than two times among the other factors, followed by the distance from drainage (0.141) and then lithology (0.117). Landslide locations were used to validate the results of the landslide susceptibility map, and the verification results showed 83% accuracy. The validation results showed sufficient agreement between the computed susceptibility map and the existing data on landslide areas.     

机器学习模型在滑坡易发性评价中的应用

机器学习在滑坡的易发性评价中面临两个难点,一是评价指标的客观量化,二是训练样本的选择。鉴于此,采用频率比法实现了评价指标的客观量化,利用k均值聚类算法实现了非滑坡样本数据的筛选。结果表明,以k均值聚类算法筛选非滑坡为前提,神经网络的训练精度由73%提升到了97%,支持向量机的训练精度由75%提升到了96%。基于GIS平台,将神经网络和支持向量机模型计算的全区易发性指数按自然断点法分为五个区域,分区图与历史灾害点的叠加分析统计结果显示,神经网络在全局范围内的评价结果优于支持向量机模型,全局精度分别为76%和74%。研究结果可为南江县的防灾减灾工作提供参考。     

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.     

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,...     

A novel hybrid integration model using support vector machines and random subspace for weather-triggered landslide susceptibility assessment in the Wuning area (China)

This study proposed a hybrid modeling approach using two methods, support vector machines and random subspace, to create a novel model named random subspace-based support vector machines (RSSVM) for assessing landslide susceptibility. The newly developed model was then tested in the Wuning area, China, to produce a landslide susceptibility map. With the purpose of achieving the objective of the study, a spatial dataset was initially constructed that includes a landslide inventory map consisting of 445 landslide regions. Then, various landslide-influencing factors were defined, including slope angle, aspect, altitude, topographic wetness index, stream power index, sediment transport index, soil, lithology, normalized difference vegetation index, land use, rainfall, distance to roads, distance to rivers, and distance to faults. Next, the result of the RSSVM model was validated using statistical index-based evaluations and the receiver operating characteristic curve approach. Then, to evaluate the performance of the suggested RSSVM model, a comparison analysis was performed to other existing approaches such as artificial neural network, Naïve Bayes (NB) and support vector machine (SVM). In general, the performance of the RSSVM model was better than the other models for spatial prediction of landslide susceptibility. The AUC results of the applied models are as follows: RSSVM (AUC = 0.857), followed by MLP (AUC = 0.823), SVM (AUC = 0.814) and NB (AUC = 0.783). The present study indicates that RSSVM can be used for landslide susceptibility evaluation, and the results are very useful for local governments and people living in the Wuning area.     

利用证据权法实现滑坡易发性区划

依托“5.12”特大地震的抗震救灾工作,以汶川地震12个极重灾县市为研究对象,在1:5万滑坡详细调查、编录和遥感影像解译的基础上,利用DEM数据,ETM影像及基础地质数据,使用证据权法完成了研究区滑坡易发性评价因子的提取与制图以及相关性统计分析,实现了1:5万的滑坡易发性区划。     

An evolutionary approach for spatial prediction of landslide susceptibility using LiDAR and symbolic classification with genetic programming

Natural Hazards - This research examines the potential of spatial prediction of landslide susceptibility by implementing an evolutionary approach using symbolic classification with genetic...     

Improving landslide susceptibility predictive power through colluvium mapping in Tegucigalpa,Honduras

Natural Hazards - Tegucigalpa, the capital city of Honduras, has the highest number of landslides recorded in the country. The city has data and information from four landslide inventories and five...     

An approach for GIS-based statistical landslide susceptibility zonation—with a case study in the Himalayas

Landslide susceptibility zonation (LSZ) is necessary for disaster management and planning development activities in mountainous regions. A number of methods, viz. landslide distribution, qualitative, statistical and distribution-free analyses have been used for the LSZ studies and they are again briefly reviewed here. In this work, two methods, the Information Value (InfoVal) and the Landslide Nominal Susceptibility Factor (LNSF) methods that are based on bivariate statistical analysis have been applied for LSZ mapping in a part of the Himalayas. Relevant thematic maps representing various factors (e.g., slope, aspect, relative relief, lithology, buffer zones along thrusts, faults and lineaments, drainage density and landcover) that are related to landslide activity, have been generated using remote sensing and GIS techniques. The LSZ derived from the LNSF method, has been compared with that produced from the InfoVal method and the result shows a more realistic LSZ map from the LNSF method which appears to conform to the heterogeneity of the terrain.     

Discussion on the tree-based machine learning model in the study of landslide susceptibility

Natural Hazards - This study reported an application of the tree-based models to landslide susceptibility. The landslide inventory and ten conditioning factors were first constructed, based on data...     

Physically-based landslide susceptibility analysis using Monte Carlo simulation in a tropical mountain basin

ABSTRACT

Physically-based distributed models are implemented for landslide susceptibility and hazard assessment around the world. Probabilistic methodologies are considered appropriate to study and quantify the uncertainties derived from the input parameters of these models. In this paper, three sets of Monte Carlo simulations, each one with 10,000 iterations, were applied for a slope stability analysis in a small basin of Envigado (Colombia), using the TRIGRS model, to characterise the uncertainty in the landslide assessment. Different parameters to determine the minimum number of realizations required to ensure a small variation in the failure probability were proposed and analyzed. The quality of the landslide susceptibility assessment was studied. Unexpected and probably erroneous results that may be common in the maps generated using this and other similar methodologies were identified and explained. Additionally, the distribution of the factor of safety was calculated for different grid cells of the basin, showing that the probability density function with the best adjustment to the frequency histogram of the factor of safety can vary between grid cells. The assumption of a normal distribution for the factor of safety would be inappropriate and would lead to miscalculations in this case study.