Landslide susceptibility mapping in an area of underground mining using the multicriteria decision analysis method

• Published in: Environmental monitoring and assessment 2018-12, Vol.190 (12), p.725-14, Article 725
• Main Authors: Arca, Deniz, Kutoğlu, Hakan Ş., Becek, Kazimierz
• Format: Article
• Language: English
• Subjects: Anthropogenic factors; Atmospheric Protection/Air Quality Control/Air Pollution; Decision analysis; Decision Support Techniques; Deformation; Earth; Earth and Environmental Science; Earth surface; Ecology; Ecotoxicology; Environment; Environmental Management; Environmental monitoring; Environmental Monitoring - methods; Environmental risk; Environmental science; Geographic Information Systems; Geographical information systems; Geology; Geomorphology; Hierarchies; Human influences; Humans; Interferometry; Land cover; Land subsidence; Landslides; Landslides & mudslides; Methods; Mining; Monitoring/Environmental Analysis; Multiple criterion; Physical characteristics; Physical properties; Radar; Risk Assessment - methods; SAR (radar); Spatial analysis; Subsidence; Synthetic aperture radar; Synthetic aperture radar interferometry; Topography (geology); Turkey; Underground mining; Urban planning
• ISSN: 0167-6369; 1573-2959
• DOI: 10.1007/s10661-018-7085-5

Landslides are geomorphological phenomena that affect anthropogenic and natural features on the Earth’s surface. Many previous studies have identified several factors that have contributed to landslides. Among these factors are physical characteristics, such as slope, aspect, and land cover, of Earth’s surface. Moreover, landslides can be triggered by human activities such as underground mining. This study aims to identify landslide susceptibility areas by analyzing landslide-related factors, including land subsidence triggered by underground mining. The area of interest was Kozlu, Turkey, where underground mining has been in progress for the past 100 years. Thus, to identify landslide risk zones, the multicriteria decision analysis method, together with the analytical hierarchy method, was used. The datasets included were topography, land cover, geological settings, and mining-induced land subsidence. The spatial extent of land subsidence was estimated using a previously published model. A landslide susceptibility map (LSM) was developed using a purposely developed GIS-based software. The results were compared with a terrain deformation map, which was developed in a separate study using the differential synthetic aperture radar interferometry (DInSAR) technique. The results showed a substantial correlation between the LSM and DInSAR map. Furthermore, it was found that ~ 88% of the very high and high landslide risk areas coincided with location of the past landslide events. These facts suggest that the algorithm and data sources used were sufficient to produce a sufficiently accurate LSM, which may be used for various purposes such as urban planning.