Comparison of Effects between Different Weight Calculation Methods for Improving Regional Landslide Susceptibility—A Case Study from Xingshan County of China

Bo Cao, Qingyi Li and Yuhang Zhu ()
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Bo Cao: College of Mining, Liaoning Technical University, Fuxin 123000, China
Qingyi Li: College of Mining, Liaoning Technical University, Fuxin 123000, China
Yuhang Zhu: Faculty of Engineering, China University of Geosciences, Wuhan 430074, China

Sustainability, 2022, vol. 14, issue 17, 1-15

Abstract: The information value (IV) model is a conventional method for landslide susceptibility prediction (LSP). However, it is inconsistent with the actual situation to regard all conditioning factors as equally weighted in the modeling process. In view of this, this paper studied the optimization effect of different weight calculation methods for IV model. Xingshan County, a typical landslide-prone area located in Hubei Province, China, was taken as a case study. The procedure was as follows: First, six conditioning factors, including elevation, slope angle, aspect, curvature, distance to river, and distance to road, were selected to form an evaluation factor library for analyzing the landslide susceptibility. Then, the weight of factors was calculated by fuzzy analytical hierarchy process (FAHP) and principal component analysis (PCA). On this basis, combined with the IV model, two weighted IV models (FAHP-IV model and PCA-IV model) were formed for LSP. The results shows that the optimization effect of PCA was the best. Moreover, compared with the IV-only model (AUC = 0.71), the FAHP-IV model (AUC = 0.76) and PCA-IV model (AUC = 0.79) performed better. The outcome also provided a feasible way for the study of regional LSP.

Keywords: landslide susceptibility assessment; information value model; fuzzy analytical hierarchy process; principal component analysis; weight (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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