Landslide susceptibility mapping by using logistic regression model with neighborhood analysis: A case study in Mizunami city

Liangjie Wang, Kazuhide Sawada, Shuji Moriguchi

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)

Abstract

Landslides which affect human lives and economic losses are always attracted a lot of concerning in modern society. In order to identify the potential hazardous areas related to landslides, three methods have been used, such as qualitative or knowledge-based method, deterministic method and quantitative-based method. Geographical information system (GIS) technology and high computing ability provide a convenient tool to deal with landslide triggering factors and make the quantitative-based method achieve effectively. In this study, landslide-related factors such as topographical elevation, slope angle, slope aspect, topographical wetness index (TWI) and stream power index (SPI), were employed in the landslide susceptibility analysis. The logistical regression was used to obtain the relationships for landslide susceptibility between landslides and causative factors. The distributions of observed landslides were used to evaluate the performance of the susceptibility map. The approaches described in this paper showed us that the logistical regression and neighborhood can be used as simple tools to predict the potential landslide locations. This map will be helpful for city planning, infrastructure construction and agriculture developments in the future.

Original languageEnglish
Pages (from-to)99-104
Number of pages6
JournalInternational Journal of GEOMATE
Volume1
Issue number2
DOIs
Publication statusPublished - 2011 Dec
Externally publishedYes

Keywords

  • GIS
  • Landslide
  • Logistic regression
  • Susceptibility map

ASJC Scopus subject areas

  • Environmental Engineering
  • Building and Construction
  • Geotechnical Engineering and Engineering Geology
  • Soil Science

Fingerprint Dive into the research topics of 'Landslide susceptibility mapping by using logistic regression model with neighborhood analysis: A case study in Mizunami city'. Together they form a unique fingerprint.

Cite this