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Geoscience ›› 2019, Vol. 33 ›› Issue (01): 235-245.DOI: 10.19657/j.geoscience.1000-8527.2019.01.23

• Engineering Geology • Previous Articles     Next Articles

Landslide Susceptibility Assessment Based on Information Value Model, Logistic Regression Model and Their Integrated Model: A Case in Shatang River Basin, Qinghai Province

LI Zetong1,2(), WANG Tao1(), ZHOU Yang3, LIU Jiamei1, XIN Peng1   

  1. 1. Institute of Geomechanics, Chinese Academy of Geological Sciences, Beijing 100081, China
    2. China University of Geosciences, Beijing 100083, China
    3. College of Environment and Civil Engineering, Chengdu University of Technology, Chengdu, Sichuan 610059, China
  • Received:2018-05-03 Revised:2018-11-30 Online:2019-02-26 Published:2019-02-28
  • Contact: WANG Tao

Abstract:

Quantitative landslide susceptibility assessment is important to predict the spatial probability of landslides. The assessment method based on statistical analysis principle is in present commonly adopted worldwide, and comparison of different assessment methods has become a research hot spot. The loess region of the Shatang River Basin in Qinghai Province was the focus of this study. Strengths and limitations of the information value and logistic regression models in landslide susceptibility assessment were analyzed and an integrated model was proposed. Seven influence factors including the slope, aspect, relief and lithology, distance from main/branch drainage, and the normalized difference vegetation index (NDVI) were analyzed and compared with the landslide susceptibility assessment results based on the three models. The results suggest that the successful rate decreases from the integrated model (78.9%), through the information value model (71.8%) to the logistic regression model (70.8%), which indicates that the performance of the latter two models are similar in the loess landslide susceptibility assessment in the Shatang River Basin, and the successful rate of the new method is obviously higher. This study provides a reference for the quantitative landslide susceptibility assessment in the loess plateaus.

Key words: landslide, susceptibility assessment, information value model, logistic regression model, integrated model

CLC Number: