An assessment on the use of logistic regression and artificial neural networks with different sampling strategies for the preparation of landslide susceptibility maps

Nefeslioglu H. A., GÖKÇEOĞLU C., SÖNMEZ H.

ENGINEERING GEOLOGY, vol.97, pp.171-191, 2008 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Review
  • Volume: 97
  • Publication Date: 2008
  • Doi Number: 10.1016/j.enggeo.2008.01.004
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.171-191
  • Hacettepe University Affiliated: Yes


The main purpose of this study is to highlight the conceptual differences of produced susceptibility models by applying different sampling strategies: from all landslide area with depletion and accumulation zones and from a zone which almost represents pre-failure conditions. Variations on accuracy and precision values of the models constructed considering different algorithms were also investigated. For this purpose, two most popular techniques, logistic regression analysis and back-propagation artificial neural networks were taken into account. The town Ispir and its close vicinity (Northeastern part of Turkey), suffered from landsliding for many years was selected as the application site of this study. As a result, it is revealed that the back-propagation artificial neural network algorithms overreact to the samplings in which the presence (1) data were taken from the landslide masses. When the generalization capacities of the models are taken into consideration, these reactions cause imprecise results, even though the area under curve (AUC) values are very high (0.915