Relative effect method of landslide susceptibility zonation in weathered granite soil: a case study in Deokjeok-ri Creek, South Korea

Pradhan, Ananta Man Singh; Kim, Yun-Tae

doi:10.1007/s11069-014-1065-z

Cited by 119 publications

(58 citation statements)

References 49 publications

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“…The modeling concept is based on field information for calibration, and the output of the models depends strongly on accurate positioning of known landslide initiation zones. A GIS-based landslide inventory map was prepared using data from previous reports (Pradhan and Kim 2014) and aerial photographs. The aerial images were orthorectified, and spatial data were obtained by extracting landslide zones by digitizing via visual interpretation using ArcGIS 10.2 software.…”

Section: Landslide Inventorymentioning

confidence: 99%

Application and comparison of shallow landslide susceptibility models in weathered granite soil under extreme rainfall events

Pradhan

Kim

2014

Environ Earth Sci

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Landslide susceptibility maps are important for risk management and land-use planning in mountainous countries such as Korea. Many of the slopes of the mountainous areas of Korea are composed of residual soils that originate from weathering of the granite bedrock. The purpose of this study was to compare the application of the SHALSTAB and SINMAP models to slope stability evaluation in Deokjeok-ri Creek. The SINMAP and SHALSTAB models are terrain stability models that are based on a steadystate hydrologic model coupled with an infinite-slope stability equation. A digital elevation model based on LIDAR data was used to calculate the slope and wetness indices. In situ and laboratory tests were performed to find the geotechnical parameters. A geographic information systembased landslide inventory map of 748 landslide locations was prepared using data from previous reports, aerial photographs, and extensive field work. This inventory of landslide scars was used to document sites of instability and to provide an evaluation of model performance by comparing the observed landslide locations with predictions from the models. Both SHALSTAB and SINMAP models identified the northern part of the catchment as the most unstable area. Receiver operator curve plots were used to show that the susceptibility maps produced using SHALSTAB had better prediction accuracy (82.4 %) than the SINMAP model (62.58 %) had. It is concluded that SHALSTAB was relatively successful in delineating slopes where shallow landslides have been observed previously. SHALSTAB is therefore more applicable to weathered granite soil.

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Section: Landslide Inventorymentioning

confidence: 99%

Application and comparison of shallow landslide susceptibility models in weathered granite soil under extreme rainfall events

Pradhan

Kim

2014

Environ Earth Sci

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“…In the case of plan curvature, the planner and convex slopes showed higher landslide frequencies than concave slopes (Figure 10h), because convex rounded hillslopes may be exposed to continuous swelling and contraction of loose debris on an inclined slope [50]. In general, higher internal relief exhibited high possibility of landslides [76]. In the study area, landslides are mainly concentrated on internal relief ranges from 47 m to 149.8 m (Figure 10i) because internal reliefs above 149.8 m consist of bare rocks without colluvial deposits.…”

Section: Landslide Inventory Distribution Analysismentioning

confidence: 98%

An Ensemble Model for Co-Seismic Landslide Susceptibility Using GIS and Random Forest Method

Shrestha

Kang

Suwal

2017

IJGI

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Abstract:The Mw 7.8 Gorkha earthquake of 25 April 2015 triggered thousands of landslides in the central part of the Nepal Himalayas. The main goal of this study was to generate an ensemble-based map of co-seismic landslide susceptibility in Sindhupalchowk District using model comparison and combination strands. A total of 2194 co-seismic landslides were identified and were randomly split into 1536 (~70%), to train data for establishing the model, and the remaining 658 (~30%) for the validation of the model. Frequency ratio, evidential belief function, and weight of evidence methods were applied and compared using 11 different causative factors (peak ground acceleration, epicenter proximity, fault proximity, geology, elevation, slope, plan curvature, internal relief, drainage proximity, stream power index, and topographic wetness index) to prepare the landslide susceptibility map. An ensemble of random forest was then used to overcome the various prediction limitations of the individual models. The success rates and prediction capabilities were critically compared using the area under the curve (AUC) of the receiver operating characteristic curve (ROC). By synthesizing the results of the various models into a single score, the ensemble model improved accuracy and provided considerably more realistic prediction capacities (91%) than the frequency ratio (81.2%), evidential belief function (83.5%) methods, and weight of evidence (80.1%).

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“…The literature contains research suggesting that landslides increase with the increase in elevation. In other words, it is shown that landslides tend to appear more in places of high altitude (Koukis and Ziourkas, 1991;Pachauri and Pant, 1992;Pachauri et al, 1998;Gökçeoğlu and Ercanoğlu, 2001;Gritzner et al, 2001;Ercanoğlu et al, 2004;Gökçeoğlu et al, 2005;Görüm, 2006;Lee and Pradhan 2006;Caniani et al, 2008;Akıncı et al, 2010;Akıncı et al, 2011;Özdemir, 2009;Özşahin and Kaymaz, 2013;Pradhan and Kim, 2014;Özşahin, 2015;Pradhan and Kim 2015;Avcı, 2016 b;Dölek and Avcı, 2016;Wu and Ke, 2016). On the contrary , there are studies in which landslide intensity decreases as elevation increases and in which landslides occur at low and medium elevations (Yüksel, 2007).…”

Section: Elevation Classesmentioning

confidence: 99%

Morphological parameters causing landslides: a case study of elevation

Çellek¹

2019

Bulletin of the Mineral Research and Exploration

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The history of landslide susceptibility maps goes back about 50 years. Danger and risk maps later followed these maps. Inventory maps provide the source of all these. There are different parameters selected specially for each field in the literature as well as parameters selected because they are easy to produce and obtain data. One of the parameters that are easy to access and prepare and are acquired from the secondary derivatives of topographical data is the parameter of elevation.This study tried to research the effect of elevation on landslides by reviewing the literature in detail. The used class ranges and elevation values were reviewed and applied to the map sections selected from Turkey. By analyzing the results, the goal was to determine at which elevation ranges landslides occurred.The study tried to investigate the effect of the parameter of elevation using data from the literature. It works to compare the elevation values for map sections selected to compare with the literature. The study comprises two stages. The first step tried to acquire statistical data by researching the data from the literature. The data were investigated in the second stage. For this purpose, close to 1,500 studies prepared between 1967 and 2019 were reviewed. According to the literature, the parameter of was used in analyses because it is easy to produce and is morphologically effective.

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Relative effect method of landslide susceptibility zonation in weathered granite soil: a case study in Deokjeok-ri Creek, South Korea

Cited by 119 publications

References 49 publications

Application and comparison of shallow landslide susceptibility models in weathered granite soil under extreme rainfall events

Application and comparison of shallow landslide susceptibility models in weathered granite soil under extreme rainfall events

An Ensemble Model for Co-Seismic Landslide Susceptibility Using GIS and Random Forest Method

Morphological parameters causing landslides: a case study of elevation

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