ORIGINAL RESEARCH
GIS-Based Landslide Susceptibility Zonation Mapping Using the Weighted Information Model in Erlang Mountain - Zheduo Mountain Power Transmission Project, China
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State Grid Sichuan Electric Power Company Economic and Technological Research Institute, Chengdu, Sichuan
 
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State Grid Sichuan Electric Power Company Ganzi Power Supply Company, Kangding, Sichuan
 
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State Key Laboratory of Geohazard Prevention and Geoenvironment Protection (Chengdu University of Technology), Chengdu, Sichuan
 
 
Submission date: 2023-08-04
 
 
Final revision date: 2023-08-30
 
 
Acceptance date: 2023-09-08
 
 
Online publication date: 2023-11-23
 
 
Publication date: 2024-01-03
 
 
Corresponding author
Feng Tian   

State Key Laboratory of Geohazard Prevention and Geoenvironment Protection (Chengdu University of Technology), China
 
 
Pol. J. Environ. Stud. 2024;33(1):609-617
 
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ABSTRACT
The power transmission and transformation projects in the western region are facing the threat of frequent seismic activities, landslides and other geological disasters. Evaluation of the landslide susceptibility in power transmission projects has important theoretical and practical significance for the selection of power transmission channels and station sites, landslide monitoring and prevention in western mountainous areas. This paper takes the Erlang Mountain-Zheduo Mountain power transmission as the research area, based on the study of characteristics of landslide, evaluation factors are selected from aspects such as meteorology, hydrology, topography, rock and soil types; a weighted information model was established by using Pearson correlation coefficient method, CRITIC weight method, and independence weight coefficient method. Based on ArcGIS technology and weighted information model, the landslide susceptibility of Erlang Mountain-Zheduo Mountain Power Transmission Project is evaluated. The ROC curves and AUC value were used to verify the effect of weighted information model, and its AUC value is 0.866, indicating that HPIV model has a good prediction effect on landslide disasters.
eISSN:2083-5906
ISSN:1230-1485
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