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Geophysical Research Abstracts,
Vol. 12, EGU2010-49, 2010
EGU General Assembly 2010
© Author(s) 2009
Landslide susceptibility mapping of a landside-prone area from Turkey by
decision tree analysis
Tolga Gorum (1), M. Celal Tunusluoglu (2), Ebru Sezer (3), Hakan A. Nefeslioglu (4), A.Selman Bozkir (3), and
Candan Gokceoglu (5)
(1) International Institute for Geo–information Science and Earth Observation (ITC), Department of EarthSystems Analysis,
P.O. Box 6, 7500 AA Enschede, The Netherlands, (2) Canakkale Onsekiz Mart University, Department of Geological
Engineering, 17020 Terzioglu Campus, Canakkale, Turkey, (3) Hacettepe University, Department of Computer Engineering,
06800, Beytepe, Ankara, Turkey, (4) General Directorate of Mineral Research and Exploration, Department of Geological
Research, Ankara, Turkey, (5) Hacettepe University, Department of Geological Engineering, 06800, Beytepe, Ankara, Turkey
The landslides are accepted as one of the important natural hazards throughout the world. Besides, the regional
landslide susceptibility assessments is one of the first stages of the landslide hazard mitigation efforts. For this
purpose, various methods have been applied to produce landslide susceptibility maps for many years. However,
application of decision tree to landslide susceptibility mapping, one of data mining methods, is not common.
Considering this lack in the landslide literature,an application of decision tree method to landslide susceptibility
mapping is the main purpose of the present study. As the study area, the Inegol region (Northwestern Turkey) is
selected. In the first stage of the study, a landslide inventory is produced by aerial-photo interpretations and field
studies. Employing 16 topographic and lithologic variables, the landslide susceptibility analyses are performed by
decision tree method. The AUC (Area Under Curve) values for ROC (Receiver-Operating Characteristics) curves
are calculated as 0.942 for the landslide susceptibility model obtained from the decision tree analysis. According to
the AUC values, the decision tree analysis presents a considerable performance. As a result of the present study, it
may be concluded that the decision tree method presents promising results for the regional landslide susceptibility
assessment. However, the technique should be studied for different landslide–prone areas and compared with other
prediction techniques such as logistic regression, artificial neural networks, fuzzy approaches, etc.