Date

2015-01-01

Author

ARİF MERT, EKER
DİKMEN, MEHMET
CAMBAZOĞLU, SELİM
DÜZGÜN, HAFİZE ŞEBNEM
Akgün, Haluk

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

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The purpose of this study was to investigate the capabilities of different landslide susceptibility methods by comparing their results statistically and spatially to select the best method that portrays the susceptibility zones for the Ulus district of the Bartin province (northern Turkey). Susceptibility maps based on spatial regression (SR), linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), logistic regression (LR) method, and artificial neural network method (ANN) were generated, and the effect of each geomorphological parameter was determined. The landslide inventory map digitized from previous studies was used as a base map for landslide occurrence. All of the analyses were implemented with respect to landslides classified as rotational, active, and deeper than 5m. Three different sets of data were used to produce nine explanatory variables (layers). The study area was divided into grids of 90mx90m, and the 'seed cell' technique was applied to obtain statistically balanced population distribution over landslide inventory area. The constructed dataset was divided into two datasets as training and test. The initial assessment consisted of multicollinearity of explanatory variables. Empirical information entropy analysis was implemented to quantify the spatial distribution of the outcomes of these methods. Results of the analyses were validated by using success rate curve (SRC) and prediction rate curve (PRC) methods. Additionally, statistical and spatial comparisons of the results were performed to determine the most suitable susceptibility zonation method in this large-scale study area. In accordance with all these comparisons, it is concluded that ANN was the best method to represent landslide susceptibility throughout the study area with an acceptable processing time.

Subject Keywords

Linear discriminant analysis, Logistic regression, Northern Turkey, Spatial regression, Quadratic discriminant analysis, Landslide susceptibility, Artificial neural network

URI

https://hdl.handle.net/11511/36539

Collections

Department of Geological Engineering, Article