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This case study presented herein compares the GIS-based landslide susceptibility mapping methods such as conditional probability (CP), logistic regression (LR), artificial neural networks (ANNs) and support vector machine (SVM) applied in Koyulhisar (Sivas, Turkey). Digital elevation model was first constructed using GIS software. Landslide-related factors such as geology, faults, drainage system, topographical elevation, slope angle, slope aspect, topographic wetness index, stream power index, normalized difference vegetation index, distance from settlements and roads were used in the landslide susceptibility analyses. In the last stage of the analyses, landslide susceptibility maps were produced from ANN, CP, LR, SVM models, and they were then compared by means of their validations. However, area under curve values obtained from all four methodologies showed that the map obtained from ANN model looks like more accurate than the other models, accuracies of all models can be evaluated relatively similar. The results also showed that the CP is a simple method in landslide susceptibility mapping and highly compatible with GIS operating features. Susceptibility maps can be easily produced using CP, because input process, calculation and output processes are very simple in CP model when compared with the other methods considered in this study.

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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.

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