The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B3-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2020, 1229–1236, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-1229-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2020, 1229–1236, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-1229-2020

  21 Aug 2020

21 Aug 2020

LANDSLIDE SUSCEPTIBILITY MAPPING WITH RANDOM FOREST MODEL FOR ORDU, TURKEY

G. Karakas1, R. Can1, S. Kocaman1, H. A. Nefeslioglu2, and C. Gokceoglu2 G. Karakas et al.
  • 1Dept. of Geomatics Engineering, Hacettepe University, 06800 Beytepe Ankara, Turkey
  • 2Dept. of Geological Engineering, Hacettepe University, 06800 Beytepe Ankara, Turkey

Keywords: Aerial Photogrammetry, Landslide Susceptibility Mapping, Machine Learning, Random Forest, Ordu (Turkey)

Abstract. Landslides are among commonly observed natural hazards all over the world and can be quite destructive for infrastructure and in settlement areas. Their occurrences are often related with extreme meteorological events and seismic activities. Preparation of landslide susceptibility maps is important for disaster mitigation efforts and to increase the resilience. The factors effective on landslide susceptibility map production depend mainly on the topography, land use and the geological characteristics of the region. The up-to-date and accurate data needed for extracting the effective parameters can be obtained by using photogrammetric techniques with high spatial resolution. Data driven ensemble methods are being increasingly used for landslide susceptibility map production and accurate results can be obtained. In this study, regional landslide susceptibility map of a landslide-prone area in a part of Ordu Province in northern Turkey is produced using topographic and lithological parameters by employing the random forest method. An actual landslide inventory delineated manually by geologists using the produced orthophotos and the digital terrain model (DTM) is used for training the model. The results show that an accuracy of 83% and precision of 92% can obtained from the data and the random forest method. The approach can be applied for generation of regional susceptibility maps semi-automatically.