A comparison of landslide susceptibility maps produced by logistic regression, multi-criteria decision, and likelihood ratio methods: a case study at Izmir, Turkey

Akgun A.

LANDSLIDES, vol.9, no.1, pp.93-106, 2012 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 9 Issue: 1
  • Publication Date: 2012
  • Doi Number: 10.1007/s10346-011-0283-7
  • Journal Name: LANDSLIDES
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.93-106
  • Keywords: Landslide, Multivariate statistics, Bivariate statistics, Susceptibility, Turkey, ARTIFICIAL NEURAL-NETWORKS, FREQUENCY RATIO, CITY-CENTER, GIS, AREA, MODEL, REGION, HAZARD, MULTIVARIATE, ASSESSMENTS
  • Karadeniz Technical University Affiliated: No


The main purpose of this study is to compare the use of logistic regression, multi-criteria decision analysis, and a likelihood ratio model to map landslide susceptibility in and around the city of Izmir in western Turkey. Parameters, such as lithology, slope gradient, slope aspect, faults, drainage lines, and roads, were considered. Landslide susceptibility maps were produced using each of the three methods and then compared and validated. Before the modeling and validation, the observed landslides were separated into two groups. The first group was for training, and the other group was for validation steps. The accuracy of models was measured by fitting them to a validation set of observed landslides. For validation process, the area under curvature (AUC) approach was applied. According to the AUC values of 0.810, 0.764, and 0.710 for logistic regression, likelihood ratio, and multi-criteria decision analysis, respectively, logistic regression was determined to be the most accurate method among the other used landslide susceptibility mapping methods. Based on these results, logistic regression and likelihood ratio models can be used to mitigate hazards related to landslides and to aid in land-use planning.