LANDSLIDE SUSCEPTIBILITY MAPPING THROUGH WEIGHTAGES DERIVED FROM STATISTICAL INFORMATION VALUE MODEL

2018 ◽  
Vol 4 (4) ◽  
pp. 10
Author(s):  
KUMAR A. ◽  
KALITA R. ◽  
SHARMA A. ◽  
RANGA V. ◽  
RAWAT J. S. ◽  
...  
Keyword(s):  
Landslide Susceptibility ◽  
Statistical Information ◽  
Susceptibility Mapping ◽  
Information Value ◽  
Landslide Susceptibility Mapping ◽  
Information Value Model ◽  
Value Model

The Landslide Susceptibility Assessment using Bi-variate Statistical Information Value Model of Chenab River Valley, Jammu and Kashmir (India)

2021 ◽  
Vol 14 (11) ◽  
pp. 44-56
Author(s):  
Abhijit S. Patil ◽  
Bidyut K. Bhadra ◽  
Sachin S. Panhalkar ◽  
Sudhir K. Powar
Keyword(s):  
Landslide Susceptibility ◽  
Statistical Information ◽  
River Valley ◽  
Information Value ◽  
Landslide Inventory ◽  
Susceptibility Map ◽  
Acceptable Result ◽  
Information Value Model ◽  
Value Model ◽  
Chenab River

Almost every year, the Himalayan region suffers from a landslide disaster that is directly associated with the prosperity and development of the area. The study of landslide disasters helps planners, decision-makers and local communities for the development of anthropogenic structures in order to enhance the safety of society. Therefore, the prime aim of this research is to produce the landslide susceptibility map for the Chenab river valley using the bi-variate statistical information value model to detect and demarcate the areas of potential landslide incidence. The object-based image analysis method identified about 84 potential sites of landslides as landslide inventory. The statistical information value model is derived from the landslide inventory and multiple causative factors. The outcome showed that 23% area of the Chenab river valley falls into the class of a very high landslide susceptibility zone. The ROC curve method is used to validate the model which denoted the acceptable result for the landslide susceptibility zonation with 0.826 AUC value for the Chenab river valley.

scholarly journals Comparison of Logistic Regression, Information Value, and Comprehensive Evaluating Model for Landslide Susceptibility Mapping

2021 ◽  
Vol 13 (7) ◽  
pp. 3803
Author(s):  
Rui-Xuan Tang ◽  
E-Chuan Yan ◽  
Tao Wen ◽  
Xiao-Meng Yin ◽  
Wei Tang
Keyword(s):  
Logistic Regression ◽  
Landslide Susceptibility ◽  
Slope Angle ◽  
Susceptibility Mapping ◽  
Information Value ◽  
Landslide Susceptibility Mapping ◽  
Slope Length ◽  
Landslide Occurrence ◽  
Conditioning Factors ◽  
Slope Structure

This study validated the robust performances of the recently proposed comprehensive landslide susceptibility index model (CLSI) for landslide susceptibility mapping (LSM) by comparing it to the logistic regression (LR) and the analytical hierarchy process information value (AHPIV) model. Zhushan County in China, with 373 landslides identified, was used as the study area. Eight conditioning factors (lithology, slope structure, slope angle, altitude, distance to river, stream power index, slope length, distance to road) were acquired from digital elevation models (DEMs), field survey, remote sensing imagery, and government documentary data. Results indicate that the CLSI model has the highest accuracy and the best classification ability, although all three models can produce reasonable landslide susceptibility (LS) maps. The robust performance of the CLSI model is due to its weight determination by a back-propagation neural network (BPNN), which successfully captures the nonlinear relationship between landslide occurrence and the conditioning factors.

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