Landslide Susceptibility Assessment Based on Slope Unit and Information Value Method in Changbai Mountain District-Reference-Cited by-同舟云学术

Landslide Susceptibility Assessment Based on Slope Unit and Information Value Method in Changbai Mountain District

Published:2024-06 Issue:2 Volume:1 Page:39-44
ISSN:3005-5695
Container-title:Journal of Civil and Transportation Engineering
language:
Short-container-title:

Author:

Song Ruixiang,Wang Shouzhi,Song Lifang,Rao Yi,Jiang Heping

Abstract

In order to evaluate the susceptibility to geological disasters in the Changbai Mountains, slopes are used as the basic evaluation unit. Under the ArcGIS platform, the information volume model was used to conduct a zoning evaluation of the susceptibility to geological hazards in the Changbai Mountains in the study area. The evaluation results show that the overall geological hazards in the Changbai Mountains present a "C" shaped distribution, with higher geological hazard risks on the outside and lower geological hazard risks on the inside. The extremely high-risk area is located in Antu County, and the high-risk area is located in the western part of Fusong County and the western part of Linjiang City. The landslide-prone areas in the Changbai Mountains (including extremely prone and highly prone areas) cover a total area of 2995km2, accounting for 19.85% of the entire region. The application of information quantity model to evaluate landslide susceptibility has high prediction accuracy. The proportion of existing landslide points falling in very prone areas and high prone areas is 72.86%, which truly reflects the objective reality.

Publisher

STEMM Institute Press

Link

http://www.stemmpress.com/uploadfile/202401/af26a89c001f2.pdf

Reference13 articles.

1. Smith, J., Zhang, P., & Liu, H. GIS-based landslide susceptibility mapping with a novel hybrid machine learning approach modulated by slope units. Geo-Environmental Disasters, 2021, 8(1):12-28.

2. Wang, L., Zhou, S., & Chen, Y. Improved landslide susceptibility mapping using random forests and information value methods in the Three Gorges Region, China. Earth Science Informatics, 2022, 15(3):1011-1023.

3. Lee, S., & Pradhan, B. Landslide hazard mapping at Selangor, Malaysia using frequency ratio and logistic regression models. Landslides, 2019, 16(2):641-659.

4. Li Chenglin, Liu Yansong, Lai Sihan, et al. Influence Factor Analysis of the Lajia Landslide Group Based on Analytic Hierarchy Process. Geospatial Information, 2023, 21(9):36-39.

5. H. Gómez, and T. Kavzoglu. Assessment of shallow landslide susceptibility using artificial neural networks in Jabonosa River Basin, Venezuela. Engineering Geology 78.1-2(2005):11-27.