Landslide susceptibility mapping at Ovacık-Karabük (Turkey) using different artificial neural network models: comparison of training algorithms

  • Aslı Can
  • Gulseren Dagdelenler
  • Murat ErcanogluEmail author
  • Harun Sonmez
Original Paper


This study aims to investigate the performances of different training algorithms used for an artificial neural network (ANN) method to produce landslide susceptibility maps. For this purpose, Ovacık region (southeast of Karabük Province), located in the Western Black Sea Region (Turkey), was selected as the study area. A total of 196 landslides were mapped, and a landslide database was prepared. Topographical elevation, slope angle, aspect, wetness index, lithology, and vegetation index parameters were taken into account for the landslide susceptibility analyses. Two different ANN structures, which were composed of single and double hidden layers, were applied to compare the effects of the ANN. Four different training algorithms, namely batch back-propagation, quick propagation, conjugate gradient descent (CGD), and Levenberg–Marquardt, were used for the training stage of the ANN models. Thus, eight different landslide susceptibility maps were produced for the study area using different ANN structures and algorithms. In order to assess the effects and spatial performances of the considered training algorithms on the ANN models, the relative operating characteristics (ROC) and relation value (rij) approaches were used. The susceptibility map produced by CGD1 has the highest AUC (0.817) and rij values (0.972). Comparison of the susceptibility maps indicated that CGD training algorithm is the slowest one among the other algorithms, but this algorithm showed the highest performance on the results.


Artificial neural network Landslide susceptibility map Ovacık (Karabuk) Training algorithm 



This research was supported by Hacettepe University Scientific Researches Coordination Section (Project No: 735). The authors would also like to thank Mr. Alphan Haktanır and Mr. Arda Öncü for their logistic support during the field studies.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  1. 1.Geological Engineering DepartmentHacettepe UniversityAnkaraTurkey

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