Landslide susceptibility assessment using the Weight of Evidence method: A case study in Xunyang area, China

PLoS One. 2021 Jan 25;16(1):e0245668. doi: 10.1371/journal.pone.0245668. eCollection 2021.


The aim of this study is to provide a landslide susceptibility map of the Xunyang District of a mountainous terrain, at the southern part of the Qin-Ba Mountain Region, which has been highly exposed to widely distributed shallow landslides over the past few decades. The Weight of Evidence (WoE) method was adopted in this research considering both the presence of a certain landslide causative factor class and the absence of remaining classes, which was used for determining a clearly spatial correlation between a landslide occurrence and the causative factors. Intrinsic factors, including geomorphological factors, geological factors, and river flow networks, and external factors of anthropogenic engineering activities in terms of density of road network were all considered and involved in the Geological Information System (GIS) environment for reconstructing the thematic layers of factor dataset. Significant assumptions prior to the analysis were emphasized to ensure conditional independence between each pair of factors for this bivariate statistical approach. In addition, a detailed landslide inventory map was constructed through field investigation and a remote sensing interpretation process at a scale of 1:50000. The thematic layers and landslide map were overlapped to obtain a spatial statistical relationship by using the frequency ratio method. At last, the validation process for the derived susceptibility map was conducted by applying the ROC curve, indicating that more than 90% of the landslides were in categories of high and moderate susceptibility zones. The causative factor classes, including the slope angles ranging from 20 to 40°, strong weathered and fractured strata, and road network density were identified to considerably influence the landslide distribution in the study area. The results have proven to be significantly meaningful for landslide hazard risk mitigation and land use management for the local authorities responsible for these fields.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • China
  • Environmental Monitoring*
  • Geographic Information Systems*
  • Landslides*
  • Risk Assessment

Grants and funding

Yanbo Cao Grant No. 41402254, Wen Fan Grant No. 41272282, National Natural Science Foundation of China Yanbo Cao Grant No. 2019ZDLSF07-0701, Yalin Nan Grant No. 2019ZDSL05-07, Department of Science and Technology of Shaanxi Province Wen Fan Grant No. 1212011220135, DDW2016-01, DDW2017-03, DDW2018-04, China Geology Survey The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.