# Landslide Susceptibility Assessment by Using Convolutional Neural Network
## 2 Study Area
### Landslide Influencing Factors
For this purpose, with the aid of the geomorphological and latitudinal changes, the related data extracted from Landsat satellite images (TMS8) and DEM data to provide the main dataset followed by rastering of the water conditions (i.e., hydrological gradient, drainage pattern, and flow gradient) as the second group of the triggering factors, has led to the improvement of the dataset. Landsat TM8 is the most recently launched Landsat satellite and carries the operational land imager (OLI) and the thermal infrared sensor (TIRS) instruments to provide more accurate data in regards to high spatial and spectral resolution (15 m \(\times\) 15 m) and proper scene size images (185 km \(\times\) 180 km) on earth. Landsat acquires about 740 scenes a day on the worldwide reference system-2 (WRS-2) path/row system, with a swath overlap varying from 7% at the equator to a maximum of approximately 85% at extreme latitudes [38]. The DEM data represents the digital elevation data of the earth's surface, which is usedfor landscape modeling, city modeling, and visualization applications, where a digital terrain model (DTM) is often required for flood or drainage modeling along with land-use studies [39]. In fact, the DEM model is a raster in a GIS environment where each cell has a Z value or height (m). DEM data is therefore a file containing the height in a particular area and a regular grid on the bare surface of the earth. Smaller cells represent greater detail in determining the elevation of the surface. There are many sources for generating a DEM, and one of these sources is satellite imagery. The DEMs extracted from these images have different resolutions depending on the type of image. The DEM data used in this study as a dataset has been provided by the USGS at 12.5 m resolution [40]. According to the dataset, the main triggering factors involved in landslide instability in the study area have been identified and are presented in Figures 4 and 5. As presented in Figure 4, slope curvature and slope angle variations between 0\({}^{\circ}\)-89\({}^{\circ}\) in the area have led to increasing sliding instability (regardless of the geological features, slopes steeper than 35\({}^{\circ}\) are susceptible to sliding). The topographical elevation change of the study site shows that it varies extensively as a result of high geo-tectonic activity in the central Alborz zone and the intercontinental tectonic plate conflict. Although the slope aspect and mass movement relationship are not clear, the slope aspect affects the site's general physiographic trend, main precipitation path, and drainage pattern, which are all considered important parameters in landslide instabilities. To evaluate the water condition in the study area, the main water condition factors, such as hydrological gradient, drainage pattern, and flow gradient (Figure 5), were extracted from Landsat satellite imagery to provide the sensitivity of the Gorzineh-khil region in generating massive movements in soil based on climatological variations that are related to a humid climate. As it is known, the presence of water in the soil is an important factor in creating instability since major landslides almost always occur after a rainstorm. In this regard, identification of the drainage pattern and catchment status as well as the flow condition can be very effective in landslide susceptibility analysis. In the meantime, the main factor for feature extraction was considered to be topographic parameters, which were at the pixel level for landslide-prone area detection. For this purpose, spectral information, topographic variations, and geo-structural landscapes were used to prepare the dataset (the dataset was identified by a layer that covered all of this mentioned information). The dataset was filtered by pre-processing (by correcting and deleting instances) and processed in ArcGIS 10.4 [41], where the integration of the layers has presented more reliable data for landslide-prone area detection.