GIS-based landslide susceptibility mapping in the Safi region,West Morocco

Bulletin of Engineering Geology and the Environment - This study aims to establish susceptibility maps for the Moroccan Safi region, which is affected by karstification processes. This process is...     

GIS based statistical and physical approaches to landslide susceptibility mapping (Sebinkarahisar,Turkey)

The case study presents GIS-aided statistically and physically based landslide susceptibility mapping in the landslide-prone Avutmus district of Sebinkarahisar (Giresun, Turkey). Field investigations, analysis of geological data and laboratory tests suggested that two important factors have acted together to cause sliding: ground water pressures and toe erosion. Frequency ratio (FR) and stability index mapping (SINMAP) were used to create the landslide susceptibility maps based on a landslide inventory; distance from drainage systems, faults and roads; slope angle and aspect; topographic elevation and topographical wetness index; and vegetation cover. Validation of the models indicated high quality susceptibility maps with the more realistic results were obtained from the statistically based FR model.     

Adaptation and comparison of expert opinion to analytical hierarchy process for landslide susceptibility mapping

In this study, an attempt was made to re-evaluate and compare landslide susceptibility in a landslide-prone area in the West Black Sea Region of Turkey, using expert opinion and the analytical hierarchy process (AHP). In order to compare the results with a landslide susceptibility study undertaken previously in the same region using the artificial neural network (ANN) method, slope angle, slope aspect, topographical elevation, topographical shape, water conditions and vegetation cover parameters were taken into consideration. Experts were asked to rate their pairwise importance and their feedback was used in the AHP to produce a landslide susceptibility map of the study region. Its validity was tested using relation value (r _ij ) and the areal frequency distribution of the actual landslides in the area. The results were satisfactory and similar to those achieved in the previous ANN study. It is concluded that AHP can be a useful methodology in landslide susceptibility assessment.      

High-resolution lidar-based landslide hazard mapping and modeling,UCSF Parnassus Campus,San Francisco,USA

Airborne lidar (light detection and ranging) was used to create a high-resolution digital elevation model (DEM) and produce landslide hazard maps of the University of California, San Francisco Parnassus Campus. The lidar DEM consisted of nearly 2.8 million interpolated elevation values covering approximately100 ha and posted on an 0.6 m horizontal grid, from which a set of 16 maps was produced. The first subset of maps showed aspects of the topography useful for landslide mapping, an engineering geological map and a qualitative slope hazard map. The second subset consisted of physics-based probabilistic landslide hazard maps for wet static, wet seismic, and dry seismic conditions. This case history illustrates the utility of lidar-based products, supplemented by field-based geological observations and physics-based probabilistic slope stability modeling, for the evaluation of existing and potential slope stability hazards on a steep and heavily forested site.      

A novel hybrid intelligent model of support vector machines and the MultiBoost ensemble for landslide susceptibility modeling

The main aim of this study is to propose a novel hybrid intelligent model named MBSVM which is an integration of the MultiBoost ensemble and a support vector machine (SVM) for modeling of susceptibility of landslides in the Uttarakhand State, Northern India. Firstly, a geospatial database for the study area was prepared, which includes 391 historical landslides and 16 landslide-affecting factors. Then, the sensitivity of different combinations of these factors for modeling was validated using the forward elimination technique. The MBSVM landslide model was built using the datasets generated from the best selected factors and validated utilizing the area under the receiver operating characteristic (ROC) curve (AUC), statistical indexes, and the Wilcoxon signed-rank test. Results show that this novel hybrid model has good performance both in terms of goodness of fit with the training dataset (AUC = 0.972) and the capability to predict landslides with the testing dataset (AUC = 0.966). The efficiency of the proposed model was then validated by comparison with logistic regression (LR), a single SVM, and another hybrid model of the AdaBoost ensemble and an SVM (ABSVM). Comparison results show that the MBSVM outperforms the LR, single SVM, and hybrid ABSVM models. Thus, the proposed model is a promising and good alternative tool for landslide hazard assessment in landslide-prone areas.

     

A case study from Koyulhisar (Sivas-Turkey) for landslide susceptibility mapping by artificial neural networks

A case study for the use of an artificial neural network (ANN) model for landslide susceptibility mapping in Koyulhisar (Sivas-Turkey) is presented. Digital elevation model (DEM) was first constructed using ArcGIS software. Relevant parameter maps were created, including geology, faults, drainage system, topographical elevation, slope angle, slope aspect, topographic wetness index, stream power index, normalized difference vegetation index and distance from roads. Finally, a landslide susceptibility map was constructed using the neural networks. The drawbacks of the method are discussed but as the validation procedures used confirmed the quality of the map produced, it is recommended the use of ANN may be helpful for planners and engineers in the initial assessment of landslide susceptibility.      

A comparative assessment of information value,frequency ratio and analytical hierarchy process models for landslide susceptibility mapping of a Himalayan watershed,India

This research work presents a comparative performance of geographic information system (GIS)-based statistical models for landslide susceptibility mapping (LSM) of the Himalayan watershed in India. A total of 190 landslide locations covering an area of 14.63 km² were identified in the watershed, using high-resolution linear imaging self-scanning (LISS IV) data. The causative factors used for LSM of the study area are slope, aspect, lithology, curvature, lineament density, land cover and drainage buffer. The spatial database has been prepared using remote sensing data along with ancillary data like geological maps. LSMs were prepared using information value (InV), frequency ratio (FR) and analytical hierarchy process (AHP) models. The validation results using the prediction rate curve technique show 89.61%, 87.12% and 88.26% area under curve values for FR, AHP and InV models, respectively. Therefore, the frequency ratio (FR) model could be used for LSM in other parts of this hilly terrain.

     

Assessment and validation of GIS-based landslide susceptibility maps: a case study from Feltrino stream basin (Central Italy)

Landslide susceptibility studies focus on producing susceptibility maps starting from landslide inventories and considering the main conditioning factors. The validity of susceptibility maps must be verified in terms of model accuracy and prediction skills. This paper deals with a GIS-based landslide susceptibility analysis and relative validation in a hilly-coastal test-area in Adriatic Central Italy. The susceptibility analysis was performed via bivariate statistics using the Landslide-Index method and a detailed (field-based) landslide inventory. Selection and mapping of conditioning factors and landslide inventories was derived from detail geomorphological analyses of the study area. The susceptibility map was validated using recent (shallow) landslides in terms of both model accuracy and prediction skills, via Success rate and Prediction rate curves, respectively. In addition, a pre-existing official landslide inventory was applied to the model to test whether it can be used when a detailed (field-based) inventory is not available, thereby extending its usability in similar physiographic regions. The outcome of this study reveals that slope and lithology are the main conditioning factor of landslides, but also highlights the key role of surficial deposits in susceptibility assessment, for both their type and thickness. The validation results show the effectiveness of the susceptibility model in both model accuracy and prediction skills given the good percentage of correctly classified landslides. Moreover, comparison of the susceptibility map with the official Regional landslides inventory proves the possibility of using the developed susceptibility model also in the absence of detailed landslide mapping, by considering inventories that are already available.     

Spatial data analysis and application of evidential belief functions to shallow landslide susceptibility mapping at Mt. Umyeon,Seoul, Korea

Predictive mapping of landslide occurrence at the regional scale was performed at Mt. Umyeon, in the southern part of Seoul, Korea, using an evidential belief function (EBF) model. To generate the landslide susceptibility map, approximately 90 % of 163 actual landslide locations were randomly selected as a training set, and about 10 % of them were used as a validation set. Spatial data sets relevant to landslide occurrence (topographic factors, hydrologic factors, forest factors, soil factors, and geologic factors) were analyzed in a geographic information system environment. In this study, landslide susceptibility was assessed on the basis of mass function assignment (belief, disbelief, uncertainty, and plausibility) and integration within a data-driven approach. The most representative of the resulting integrated susceptibility maps (the belief map) was validated using the receiver operating characteristic (ROC) method. The verification result showed that the model had an accuracy of 74.3 % and a predictive accuracy of 88.1 %. The frequency ratio (FR) model was also used for comparison with the EBF model. Prediction and success rates of 72.1 and 85.9 % were achieved using the FR model. The validation results showed satisfactory agreement between the susceptibility map and the existing landslide location data. The EBF model was more accurate than the FR model for landslide prediction in the study area. The results of this study can be used to mitigate landslide-induced hazards and for land-use planning.     

Assessment of seismically-induced landslide susceptibility after the 2015 Gorkha earthquake,Nepal

Bulletin of Engineering Geology and the Environment - The Gorkha earthquake (MW 7.8) occurred on 25 April 2015 with its epicenter in the central part of the Gorkha District, Nepal. The earthquake...     

Factor analysis for the statistical modeling of earthquake-induced landslides

Earthquake-induced landslides are difficult to assess and predict owing to the inherent unpredictability of earthquakes. In most existing studies, the landslide potential is statistically assessed by collecting and analyzing the data of historical landslide events and earthquake observation records. Unlike rainfall-induced landslides, earthquake-induced landslides cannot be predicted in advance using real-time monitoring systems, and the development of the models for these landslides should instead depend on early earthquake warnings and estimations. Hence, in this study, factor analysis was performed and the frequency distribution method was employed to investigate the potential risk of the landslides caused by earthquakes. Factors such as the slope gradient, lithology (geology), aspect, and elevation were selected and classified as influential factors to facilitate the construction of a landslide database for the area of study.     

Three-dimensional geotechnical modeling of the soils in Riyadh city,KSA

A standard penetration test (SPT) was carried out for 700 samples from 143 boreholes in four districts in Riyadh city, Kingdom of Saudi Arabia (KSA). Rock quality designation (RQD) and unconfined compression strength (UCS) tests were also performed for 238 samples from 154 boreholes in 15 districts of the city. Three-dimensional (3D) models of the SPT, RQD, and UCS were produced using the Voxler 3 software package. Further, 333 soil samples collected from 106 boreholes in 11 districts were examined to spatially model the distinctive geotechnical patterns of the alluvial soils in two dimensions. Tests were carried out to determine the soil grain size distribution, natural water content (NWC%), Atterberg’s consistency limits [liquid limit (LL%), plastic limit (PL%), and plasticity index (PI%)], and soil–water chemical components (pH Cl⁻, SO₃²⁻, and CO₃⁻). Spatial maps of the geotechnical parameters were produced by applying the geostatistical ordinary kriging implemented in ArcGIS. Soil samples were classified according to the unified soil classification system (USCS), and a thickness of the silty clay layer was produced. Plasticity charts indicated that the soils are inorganic cohesive clays with low and moderate plasticity (CL). Soil strength parameters showed wide ranges of UCS (average 220, range 21.3–618 kg/cm²), SPT (average 39, 0–100 N), and RQD (average 44, 11–78%). UCS and SPT 3D models clarified a regional southeastward trend of increase. RQD 3D models showed poor to fair engineering quality of rocks (25–75%). The results presented here can help to establish geohazard zonation maps with construction favorability ratings for safe urban expansion.