Landslide Susceptibility Assessment Using Bagging Ensemble Based Alternating Decision Trees, Logistic Regression and J48 Decision Trees Methods: A Comparative Study

2017 ◽  
Vol 35 (6) ◽  
pp. 2597-2611 ◽  
Author(s):  
Binh Thai Pham ◽  
Dieu Tien Bui ◽  
Indra Prakash
Keyword(s):  
Logistic Regression ◽  
Comparative Study ◽  
Decision Trees ◽  
Landslide Susceptibility ◽  
Susceptibility Assessment ◽  
Landslide Susceptibility Assessment ◽  
Bagging Ensemble

Methods of Evaluation for Region’s Landslide Susceptibility. Short Overview

2017 ◽  
Vol 6 (3) ◽  
pp. 57-60
Author(s):  
Денис Кривогуз ◽  
Denis Krivoguz
Keyword(s):  
Neural Networks ◽  
Logistic Regression ◽  
Artificial Neural Networks ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Landslide Susceptibility ◽  
Susceptibility Assessment ◽  
Linear Discriminant ◽  
Landslide Susceptibility Assessment ◽  
Artificial Neural

Modern approaches to the region’s landslide susceptibility assessment are considered in this paper. Have been presented descriptions of the most used techniques for landslide susceptibility assessment: logistic regression, indicator validity, linear discriminant analysis and application of artificial neural networks. These techniques’ advantages and disadvantages are discussed in the paper. The most suitable techniques for various conditions of analysis have been marked. It has been concluded that the most acceptable techniques of analysis for a large number of input data related to the studied region are the method of logistic regression and indicator validity method. With these methods the most accurate results are achieved. When there is a lack of information, it is more expedient to use linear discriminant analysis and artificial neural networks that will minimize potential analysis inaccuracies.

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