Investigation of effective factors and landslide susceptibility zonation using the Dempster-Shafer model (case study: Middle Mazlaghan Chai)

Document Type : Research

Author

Lecturer of Geography Department, Payame Noor University, Iran

10.22092/wmrj.2023.360607.1502

Abstract

The purpose of this research is identifying effective factors on landslides and landslide susceptibility zonation with use Dempster-Shafer model in Middle Mazlaghan Chai.
In order to achieve this goal an inventory landslide map was prepared in environment of GIS as a dependent variable. With use of field works and research related to subject, Investigated and determined 9 effective factors, including slope, aspect, elevation, distance to fault, distance to road, distance from networks, land use, lithology and precipitation. After preparing the information layers and weighting in the GIS, a landslide zoning map was prepared and classified using Dempster Shaffer method. Finally, the efficiency of the Dempster Shafer method was evaluated using the area under curve and the receiver operating characteristic
The results of investigation effective factors in causing landslides showed that limestone units, alternating tuff and lava with acidic composition, distance to road more than 200 meters, distance to fault up to 200 meters, north aspect, distance More than 100 meters from networks, more than 450 mm of rainfall, more than 40% slope, medium rangeland and good range land uses and altitude more than 2600 meters have the greatest effect on landslides. Also, the results showed that the Dempster-Shafer model with the area under the curve of 0.849 has provided an acceptable accuracy for preparing the landslide susceptibility map.
On the other hand, results showed that about 22% of the watershed was located in high and very high-risk areas, but about 75% of landslides are in these areas.

Keywords



Articles in Press, Accepted Manuscript
Available Online from 21 December 2022
  • Receive Date: 16 November 2022
  • Revise Date: 05 December 2022
  • Accept Date: 21 December 2022