National level landslide susceptibility assessment of Turkey utilizing public domain dataset

Date

2016-05-01

Author

Okalp, Kivanc
Akgün, Haluk

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Landslide studies have been integrated into geographic information systems with the help of technological developments using several methods like inventory, heuristic, statistic and deterministic methods in the recent years. However, since a nationwide landslide susceptibility zoning map has not been produced for the entire territory of Turkey, this study aims to produce a landslide susceptibility map of Turkey at a national scale by utilizing publicly available datasets. In order to develop a landslide susceptibility map of Turkey at the scale of 1: 2,000,000, an index-based calculation, which considers six factors (slope, lithology, local relief, rainfall, land use, seismicity) that covers the entire territory of Turkey and controls the occurrence of landslides, was applied in a 500 x 500 m pixel resolution. Each layer (factor) having various effects on landslide susceptibility has been merged into the model with assigned weights. Four different weight groups were assigned to the layer sets through expert judgement in order to capture the layer variability for landslide susceptibility in Turkey. The performances of four different weight groups were compared and evaluated by using a receiver operator characteristics curve for minimizing the uncertainty of expert judgement procedure. It was observed that the W-3 group was superior to the other weight groups in prediction skills. The susceptibility map of W3 has been classified into five groups: no, low, moderate, high and very high susceptibility. The no susceptibility class represents 4.2 % of the Turkish territory (plains and low hills), low susceptibility class 36.4 %, medium susceptibility 8.3 %, high susceptibility 47.5 % and very high susceptibility class 3.6 %, mostly in the western and middle Black Sea regions, respectively.

Subject Keywords

Small scale, Large datasets, Country-wide, Index mapping, Index mapping, Qualitative analysis

URI

https://hdl.handle.net/11511/40652

Collections

Department of Geological Engineering, Article