A comparative study of statistical index and certainty factor models in landslide susceptibility mapping: a case study for the Shangzhou District, Shaanxi Province, China

2015 ◽  
Vol 8 (11) ◽  
pp. 9079-9088 ◽  
Author(s):  
Chengxi Zhao ◽  
Wei Chen ◽  
Qiqing Wang ◽  
Yanli Wu ◽  
Bo Yang
Keyword(s):  
Comparative Study ◽  
Landslide Susceptibility ◽  
Factor Models ◽  
Susceptibility Mapping ◽  
Shaanxi Province ◽  
Landslide Susceptibility Mapping ◽  
Certainty Factor ◽  
Statistical Index

scholarly journals Application of weights-of-evidence and certainty factor models and their comparison in landslide susceptibility mapping at Haraz watershed, Iran

2012 ◽  
Vol 6 (7) ◽  
pp. 2351-2365 ◽  
Author(s):  
Hamid Reza Pourghasemi ◽  
Biswajeet Pradhan ◽  
Candan Gokceoglu ◽  
Majid Mohammadi ◽  
Hamid Reza Moradi
Keyword(s):  
Landslide Susceptibility ◽  
Factor Models ◽  
Weights Of Evidence ◽  
Susceptibility Mapping ◽  
Landslide Susceptibility Mapping ◽  
Certainty Factor

scholarly journals Quantitative Assessment of Landslide Susceptibility Comparing Statistical Index, Index of Entropy, and Weights of Evidence in the Shangnan Area, China

Entropy ◽  
2018 ◽  
Vol 20 (11) ◽  
pp. 868 ◽  
Author(s):  
Jie Liu ◽  
Zhao Duan
Keyword(s):  
Landslide Susceptibility ◽  
Weights Of Evidence ◽  
Susceptibility Mapping ◽  
Shaanxi Province ◽  
Landslide Susceptibility Mapping ◽  
Training Dataset ◽  
Susceptibility Map ◽  
Statistical Index ◽  
Index Of Entropy ◽  
Testing Dataset

In this study, a comparative analysis of the statistical index (SI), index of entropy (IOE) and weights of evidence (WOE) models was introduced to landslide susceptibility mapping, and the performance of the three models was validated and systematically compared. As one of the most landslide-prone areas in Shaanxi Province, China, Shangnan County was selected as the study area. Firstly, a series of reports, remote sensing images and geological maps were collected, and field surveys were carried out to prepare a landslide inventory map. A total of 348 landslides were identified in study area, and they were reclassified as a training dataset (70% = 244 landslides) and testing dataset (30% = 104 landslides) by random selection. Thirteen conditioning factors were then employed. Corresponding thematic data layers and landslide susceptibility maps were generated based on ArcGIS software. Finally, the area under the curve (AUC) values were calculated for the training dataset and the testing dataset in order to validate and compare the performance of the three models. For the training dataset, the AUC plots showed that the WOE model had the highest accuracy rate of 76.05%, followed by the SI model (74.67%) and the IOE model (71.12%). In the case of the testing dataset, the prediction accuracy rates for the SI, IOE and WOE models were 73.75%, 63.89%, and 75.10%, respectively. It can be concluded that the WOE model had the best prediction capacity for landslide susceptibility mapping in Shangnan County. The landslide susceptibility map produced by the WOE model had a profound geological and engineering significance in terms of landslide hazard prevention and control in the study area and other similar areas.

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