Combining bivariate and multivariate statistical analyses to assess landslide susceptibility in the Chen-Yu-Lan watershed, Nantou, Taiwan

Thi To Ngan Nguyen, Cheng Chien Liu

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)


This paper uses quantitative approaches to generate a landslide susceptibility map (LSM) for the Chen-Yu-Lan watershed, a mountainous area in central Taiwan. Landslide inventory data, which was obtained from Formosat-2 images collected over 8 yr from 2004 to 2011, were applied to produce the LSM. Bivariate and multivariate statistical analyses were then applied to calculate a landslide susceptibility index, with the weights of the parameters computed based on landslide data from 2004 to 2011. Multivariate algorithms were used to examine the settings of various factors with regard to predicting landslides, and the results were compared with the actual history of landslides. Historical landslide data were also used to assess and classify landslide susceptibility levels, and based on this the relationship between landslide susceptibility and repeated landslides was derived. The results lead to the production of an LSM which can express the landslide susceptibility level in a range from safe to dangerous using specific values, and these predictions were then compared to data on actual landslides in the study area. Landslides are affected by various factors, each of which has a different effect, and an LSM is generated based on some of these. Therefore, knowing the various impact levels of the related factors, such as slope, aspect, drainage density, lithology and land use, will help in calculating more accurate landslide susceptibility indexes. These levels, which range from significant to very significant, are also considered in this work through examinations of the logical relationships among the weights and characteristics of the various classes of the related factors. It is anticipated that the result of this work can be used by planning managers to help identify high risk landslide areas, and those that are safe for building and other human activities.

Original languageEnglish
Pages (from-to)257-271
Number of pages15
JournalSustainable Environment Research
Issue number4
Publication statusPublished - 2014

All Science Journal Classification (ASJC) codes

  • Environmental Engineering
  • Renewable Energy, Sustainability and the Environment
  • Water Science and Technology
  • Waste Management and Disposal
  • Pollution


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