The effect of the sampling strategies on the landslide susceptibility mapping by conditional probability and artificial neural networks

Abstract

This study presented herein compares the effect of the sampling strategies by means of landslide inventory on the landslide susceptibility mapping. The conditional probability (CP) and artificial neural networks (ANN) models were applied in Sebinkarahisar (Giresun-Turkey). Digital elevation model was first constructed using a geographical information system software and parameter maps affecting the slope stability such as geology, faults, drainage system, topographical elevation, slope angle, slope aspect, topographic wetness index, stream power index and normalized difference vegetation index were considered. In the last stage of the analyses, landslide susceptibility maps were produced applying different sampling strategies such as; scarp, seed cell and point. The maps elaborated were then compared by means of their validations. Scarp sampling strategy gave the best results than the point, whereas the scarp and seed cell methods can be evaluated relatively similar. Comparison of the landslide susceptibility maps with known landslide locations indicated that the higher accuracy was obtained for ANN model using the scarp sampling strategy. The results obtained in this study also showed that the CP model can be used as a simple tool in assessment of the landslide susceptibility, because input process, calculations and output process are very simple and can be readily understood.

Landslide susceptibility analysis in data-scarce regions: the case of Kyrgyzstan

“…It helps in identifying locations of previous landslides in order to be able to predict future failures. There is no agreement on the technique for the preparation of landslide inventory maps; researchers usually adopt different inventory maps where landslides are shown as points, scarp, and seed cells (Yilmaz 2010). Small-scale maps may only show landslide locations (points strategy), as due to the scale of the map, it is not possible to draw the landslide extension.…”

Probabilistic frequency ratio (PFR) model for quality improvement of landslide susceptibility mapping from LiDAR-derived DEMs

“…Moreover, they have applied the logistic regression model to landslide hazard mapping (Lee and Pradhan 2006;Choi et al 2012). Recently, landslide hazard evaluation carried out by using fuzzy logic, and artificial neural network models Yilmaz 2010;Lee et al 2014). During the last decade, researchers indicated that landslide susceptibility and deformation measurement have extensively performed particularly for the landslides assessment (Luzi et al 2000;Schulz 2004;Su and Bork 2006;Streutker and Glenn 2006;Schulz 2007).…”

“…However, the weakness is that the points collected from theodolites, photogrammetry, levels and GNSS, satellite imageries, perform quite low in density. For example, McKean and Roering (2003) studied the low-density digital elevation model (DEM) to determine the potential to differentiate morphologically components within a landslide (Lee and Dan 2005;Glen et al 2006; Lee and Pradhan 2006;Yilmaz 2010;Niculită 2016). They explored how to provide insight into the material type and activity of the slide.…”

The spatial prediction of landslide susceptibility applying artificial neural network and logistic regression models: A case study of Inje, Korea

“…Previous studies have applied probabilistic models including an AHP: Analytic Hierarchy Process, Arti cial Neural Network, Dempster-Shapfer theory of evidence, fuzzy logic and Monte Carlo methods [1][2][3][4][5][6][7][8][9][10][11] among statistical models, the logistic regression model has also been applied to landslide susceptibility mapping [12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27]. More sophisticated assessments have involved weight of evidence approaches and frequency ratio [25][26][27][28][29][30][31][32][33][34][35][36][37][38] Research on rainfall probability calculation has primarily been limited to improving the rainfall probability predictions accuracy and to studies targeting water resources [39][40][41][42][43]. Recently, analysis of lan...…”