Comparative analysis of statistical methods for landslide susceptibility mapping in the Bostanlik District, Uzbekistan

2019 ◽  
Vol 653 ◽  
pp. 801-814 ◽  
Author(s):  
Mukhiddin Juliev ◽  
Martin Mergili ◽  
Ismail Mondal ◽  
Bakhtiar Nurtaev ◽  
Alim Pulatov ◽  
...  
Keyword(s):  
Comparative Analysis ◽  
Statistical Methods ◽  
Landslide Susceptibility ◽  
Susceptibility Mapping ◽  
Landslide Susceptibility Mapping

scholarly journals DEM Resolutions for Landslide Susceptibility Modeling

2021 ◽  
Author(s):  
Azemeraw Wubalem
Keyword(s):  
Statistical Methods ◽  
Spatial Resolution ◽  
Landslide Susceptibility ◽  
Frequency Ratio ◽  
Good Accuracy ◽  
Susceptibility Mapping ◽  
Landslide Susceptibility Mapping ◽  
Certainty Factor ◽  
Dem Resolution ◽  
Predictive Rate

Abstract In landslide susceptibility mapping, the digital elevation model (DEM) is one of the most essential data sets, which is frequently used. Therefore, evaluate the effects of the spatial resolution of DEM on the landslide susceptibility model is very important. Hence, this paper is analyzed only the effects of the spatial resolution of DEM, Advanced Spaceborne Thermal Emission, and Reflection (ASTER) was used for DEM data source. The ASTER DEM was resampled to 45, 60, 75, and 90 m spatial resolutions. A set of geodatabases were built using Geographic Information System (GIS), which contains landslide governing factors and landslide inventory. Frequency ratio (FR) and certainty factor (CF) statistical methods were employed to generate a landslide susceptibility map. Landslide density and area under the curve (AUC) were applied to evaluate the model's performance for each DEM resolution. The results of the predictive rate curve value of AUC showed a coarser DEM resolution (90 m) produced the best performance and prediction accuracy. This indicated that a coarser DEM resolution produced higher predictive accuracy than fine resolution. Concerning the statistical models, the frequency ratio model produced very good accuracy at the coarser DEM resolutions (75 and 90 m). The predictive rate curve value of AUC ranges from 86–92% for the FR model and 81–89% for the CF model which indicating very good accuracy of the models to predict future landslide incidence in the study area. Therefore, it is possible to endorse statistical methods (frequency ratio, and certainty factor) respect with to DEM resolution, is satisfactory to landslide susceptibility mapping.

scholarly journals Landslide Susceptibility Mapping by Comparing GIS-Based Bivariate Methods: A Focus on the Geomorphological Implication of the Statistical Results

2021 ◽  
Vol 13 (21) ◽  
pp. 4280
Author(s):  
Laura Coco ◽  
Debora Macrini ◽  
Tommaso Piacentini ◽  
Marcello Buccolini
Keyword(s):  
Statistical Methods ◽  
Landslide Susceptibility ◽  
Statistical Approach ◽  
Susceptibility Mapping ◽  
Landslide Susceptibility Mapping ◽  
Catchment Scale ◽  
Predisposing Factor ◽  
Susceptibility Maps ◽  
Reproducible Method ◽  
First Time

Landslide susceptibility is one of the main topics of geomorphological risk studies. Unfortunately, many of these studies applied an exclusively statistical approach with little coherence with the geomorphodynamic models, resulting in susceptibility maps that are difficult to read. Even if many different models have been developed, those based on statistical techniques applied to slope units (SUs) are among the most promising. SU segmentation divides terrain into homogenous domains and approximates the morphodynamic response of the slope to landslides. This paper presents a landslide susceptibility (LS) analysis at the catchment scale for a key area based on the comparison of two GIS-based bivariate statistical methods using the landslide index (LI) approach. A new simple and reproducible method for delineating SUs is defined with an original GIS-based terrain segmentation based on hydrography. For the first time, the morphometric slope index (MSI) was tested as a predisposing factor for landslides. Beyond the purely statistic values, the susceptibility maps obtained have strong geomorphological significance and highlight the areas with the greatest propensity to landslides. We demonstrate the efficiency of the SU segmentation method and the potential of the proposed statistical methods to perform landslide susceptibility mapping (LSM).

scholarly journals DEM Resolutions for Landslide Susceptibility Modeling

2021 ◽  
Author(s):  
Azemeraw Wubalem
Keyword(s):  
Statistical Methods ◽  
Spatial Resolution ◽  
Landslide Susceptibility ◽  
Frequency Ratio ◽  
Good Accuracy ◽  
Susceptibility Mapping ◽  
Landslide Susceptibility Mapping ◽  
Certainty Factor ◽  
Dem Resolution ◽  
Predictive Rate

Abstract In landslide susceptibility mapping, the digital elevation model (DEM) is one of the most essential data sets, which is frequently used. Therefore, evaluate the effects of the spatial resolution of DEM on the landslide susceptibility model is very important. Hence, this paper is analyzed only the effects of the spatial resolution of DEM, Advanced Spaceborne Thermal Emission, and Reflection (ASTER) was used for DEM data source. The ASTER DEM was resampled to 45, 60, 75, and 90 m spatial resolutions. A set of geodatabases were built using Geographic Information System (GIS), which contains landslide governing factors and landslide inventory. Frequency ratio (FR) and certainty factor (CF) statistical methods were employed to generate a landslide susceptibility map. Landslide density and area under the curve (AUC) were applied to evaluate the model's performance for each DEM resolution. The results of the predictive rate curve value of AUC showed a coarser DEM resolution (90 m) produced the best performance and prediction accuracy. This indicated that a coarser DEM resolution produced higher predictive accuracy than fine resolution. Concerning the statistical models, the frequency ratio model produced very good accuracy at the coarser DEM resolutions (75 and 90 m). The predictive rate curve value of AUC ranges from 86-92% for the FR model and 81-89% for the CF model which indicating very good accuracy of the models to predict future landslide incidence in the study area. Therefore, it is possible to endorse statistical methods (frequency ratio, and certainty factor) respect with to DEM resolution, which is satisfactory to landslide susceptibility mapping.

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