Landslide Hazard Prediction Map Based on Logistic Regression Model for Applying in the Whole Country of South Korea

2014 ◽  
Vol 14 (6) ◽  
pp. 117-123 ◽  
Author(s):  
Choongshik Woo ◽  
Hyunjung Kwon ◽  
Changwoo Lee ◽  
Kyongha Kim
Keyword(s):  
Logistic Regression ◽  
South Korea ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Landslide Hazard

scholarly journals GIS − Based Logistic Regression Models for Landslide Hazard Mapping: A Case Study From Ensaro District, North Ethiopia

2020 ◽  
Author(s):  
Abdurohman Adem ◽  
Suryabhagavan Venkata Karuturi ◽  
Tarun Kumar Raghuvanshi
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Google Earth ◽  
Landslide Hazard ◽  
Slope Aspect ◽  
Hazard Evaluation ◽  
Hazard Mapping ◽  
Data Set ◽  
Causative Factors

Abstract The present study was undertaken to identify landslides hazard prone areas in North Ethiopia. The landslide hazard in the present study area was evaluated by using the logistic regression model. Seven landslide causative factors were used for the landslide hazard evaluation, these are; slope gradient, slope aspect, elevation, proximity to streams, land-use/ land-cover, lithology and Normalized Difference Vegetation Index. Besides, for the present study landslides inventory data for the period of 2000 to 2018 was collected from the field survey and the Google earth image interpretation. The coefficient for the considered causative factors and classes were used for the identification of landslides hazard index using raster tool in ARCGIS environment. The prediction of the logistic regression model reveals that one third of the study area (32%) is under high hazard zone and the steep slopes and the elevated areas are most susceptible areas. The predicted landslides hazard zonation map is highly correlated with the training data set where 74% of it lies in the very high and high landslide hazard zones. Results of the area under the Receiver Operating Characteristic curve for the training sample, was found to be 0.76 while the area under the ROC curve of the validation sample was 0.71. Thus, the validation results has confirmed the rationality of adopted methodology, considered causative factors and their evaluation in producing LHZ map for the area. Further, the study has forwarded recommendations that can be followed to prevent and mitigate the adverse impact of landslides in the study area.

Spatial Landslide Hazard Prediction Using Rainfall Probability and a Logistic Regression Model

2014 ◽  
Vol 47 (5) ◽  
pp. 565-589 ◽  
Author(s):  
Saro Lee ◽  
Joong-Sun Won ◽  
Seong Woo Jeon ◽  
Inhye Park ◽  
Moung Jin Lee
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Landslide Hazard

Independent predictors for functional outcome after drainage of chronic subdural hematoma identified using a logistic regression model

2020 ◽  
Vol 64 (2) ◽  
Author(s):  
Sotirios Katsigiannis ◽  
Christina Hamisch ◽  
Boris Krischek ◽  
Marco Timmer ◽  
Anastasios Mpotsaris ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Functional Outcome ◽  
Subdural Hematoma ◽  
Logistic Regression Model ◽  
Chronic Subdural Hematoma
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