## 3 Materials and Methods
### Methodology
#### 3.2.1 Data Preprocessing  
The main data sources used in this study are the lithology map, the landslide inventory and the DEM. The preprocessing steps can be categorized as: (i) geometric preprocessing and the rasterization of the lithology map; (ii) extraction of topographic derivatives; (iii) preparation of the landslide inventory for the modeling process; (iv) training and test data selection via a random sampling strategy. The lithology map was first transformed into the coordinate reference system of the DEM (ETRS-LAEA with the EPSG code 3035) from the geographical coordinate system WGS4 (EPSG:4326) projection. The vector polygons defining the lithological units were merged to create a single geometry per unit as a preparation step for rasterization. The IDs ranging from one to twenty-seven were used as intensity values in the rasterization process and the rasterized lithology map was produced and stored in the GeoTIFF format. Each pixel in the raster lithology map has a value attribute from one to twenty to represent the lithological units. The coordinate system transformation and the rasterization processes were performed using the open source GDAL/OGR [37] and GeoPandas [38] libraries in the Python3.7 environment. The topographic derivatives were extracted using the open source SAGA GIS software [39]. The slope orientation, slope gradient, plan and profile curvatures, stream power index (SPI) and the topographic wetness index (TWI) features, which are among the commonly used conditioning factors (e.g., see [15; 21; 40]) were produced from the DEM. In addition, the drainage density map was produced using the DEM in ArcGIS software from ESRI, Redlands, CA, USA [41]. The SPI was calculated with Equation (1) [42], and shows the erosive power of flowing water [43]. The TWI Equation (2) explains the effect of topography for runoff generation based on the location and the size of source areas [42]. The curvature parameters are the second derivatives of DEMs and define the changes in the first derivatives (i.e., slope gradient and slope aspect) in a certain direction [43]. \[\mathrm{SPI}=\mathrm{A_{s}}\times\mathrm{tan}\ \beta \tag{1}\]  
where, \(\mathrm{A_{s}}\) is the catchment area (m\({}^{2}\) m\({}^{-1}\)) and \(\beta\) is the slope gradient (\({}^{\circ}\)). \[\mathrm{TWI}=\mathrm{ln}(\frac{\mathrm{A_{s}}}{\mathrm{tan}\ \beta}) \tag{2}\]  
Figure 4: The overall methodological workflow. In the third stage regarding the preprocessing of the landslide inventory, five landslides (depicted with the red polygons in Figure 1) were removed from the landslide inventory map for validation purposes. The main idea behind was that the methodology used here classifies landslide and non-landslide samples per pixel and using polygons (or areas) that were not employed for training or testing would allow to analyze the behavior of the classification model better. Thus, only the blue polygons shown in Figure 1 were employed in the training process. The landslide inventory feature used for the model training and testing was the rasterized inventory map, in which each pixel has a value as 0 (non-landslide) or 1 (landslide). The pixels inside the removed landslide polygons have no-data value and thus were not considered in the training step. The rasterization of the landslide inventory map was performed by using the open source GDAL library in Python. As the last step, for the training and test data selection, index values of the pixels, which were labelled as either landslide or non-landside, were selected and stored separately. The total numbers of landslide and non-landslide pixels were 108,738 and 3,281,991, respectively. A random sampling strategy was applied to non-landslide pixels in order to create a random subset for eliminating the class imbalance problem caused by the small number of landslide pixels. The ratio of landslide/non-landslide pixels was selected as 1:1.5. In the sampling step, the index values of a total of 163,107 non-landslide pixels were selected randomly.