Evaluation and comparison of landslide susceptibility mapping methods: a case study for the Ulus district, Bartın, northern Turkey

Abstract

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 Bartın 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 5 m. Three different sets of data were used to produce nine explanatory variables (layers). The study area was divided into grids of 90 m × 90 m, 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.

Exploiting Maximum Entropy method and ASTER data for assessing debris flow and debris slide susceptibility for the Giampilieri catchment (north‐eastern Sicily, Italy)

“…Common practises involve the use of stochastic and/or data mining methods relying on presence/absence techniques (e.g., Eker et al, 2014;Ermini et al, 2005;Pourghasemi et al, 2013;Lombardo et al, 2014) for calibrating the predictive model. In this research we decided to pursue a presence-only approach, which has recently been introduced within the landslide scientific community (Convertino et al, 2013;Davis et Sims, 2013;Park, 2014) by applying the Maximum Entropy (MaxEnt) algorithm (Elith et al, 2011;Phillips et al, 2004 and2006;Phillips and Dudík, 2008), which does not rely on the contribution of negative (no-landslide or absence) cases for calibration.…”

A data-driven approach for landslide susceptibility mapping: a case study of Shennongjia Forestry District, China

“…For instance, Yilmaz (2009) assessed landslide susceptibility with four methods (conditional probability, logistic regression, artificial neural network, and support vector machine) and concluded that the difference in the prediction accuracy of the four different methods was within 0.2%. Eker et al (2014) compared five methods for landslide susceptibility evaluation (linear discriminant analysis, quadratic discriminant analysis, artificial neural network, logistic regression, and spatial regression) and drew similar conclusions that the five different methods have a similar prediction accuracy with a largest difference of less than 0.5%. As the comparison analysis revealed, with the same data processing, there is little difference in the prediction accuracy between the above methods (Melchiorre et al 2008;Zhang et al 2016).…”

Performance evaluation and sensitivity analysis of ASTER and SRTM (30m) DEM derived terrain variables in landslide susceptibility assessment: a case from the Western Ghats

“…The majority of the variables used in the analysis were derived from the digital elevation models in order to analyse the sensitivity of LSZ maps. Information and database regarding the palaeoslide locations are considered as the most crucial information needed to generate the LSZ map of any area (Ayalew et al, 2004, Yilmaz, 2009, Pourghasemi et al, 2014, Regmi et al, 2014, Eker et al, 2015. In the present analysis, 52 landslide initiation locations were mapped from the field.…”