# Landslide Susceptibility Assessment by Using Convolutional Neural Network
## 2 Study Area
### Landslide Influencing Factors  
A successful landslide susceptibility analysis requires the collection of comprehensive information on land-sliding. In the meantime, the differentiation of the identified triggering factors can be a very important step. [PERSON] and [PERSON] [37] have classified the landslide triggering factors into topographic, geological, climatological, seismological, and human activity-related classes, which all depend on the environmental conditions. Landslide triggering factors are the effective factors that influence landslide movements but may/may not be the cause of sliding. The triggering factors selection is considered an important step in providing the inventory database or primary dataset that is used in landslide susceptibility assessment. In this regard, comprehensive field surveys and remote sensing observations are required, which lead to providing detailed information about the land-sliding in the region. Selection of the triggering factors required several considerations related to the dependency of triggering elements, measurability, non-redundancy, and relevance of geological characteristics. The main factors influencing landslide occurrence were identified by preparing a spatial landslide inventory database that included the spatiotemporal distribution of historical landslides and a set of potential influencing factors. Based on a field study and reviews of the Ministry of Agriculture Jihad-Mazandaran Branch (MAJ) and Forests, Range, and Watershed Management Organization (FRWMO) reports in the studied region, major recorded landslide events which are related to geomorphological/topographical changes exist in the region. The other factors, such as geological, climatological, seismological, and human-related activities, have indirect effects with respect to this main factor. During the field survey, it appeared that the reported geomorphologic, topographic, and water conditions as triggering factors concluded slope curvature, topographical elevation, slope aspect, weathering, hydrological gradient, drainage pattern, and flow gradient play considerable roles in landslide occurrence. On the other hand, the presence of thick sediment layers (soil) in the region has obscured the main geological structures in the rural areas where landslides have mostly occurred as shallow massive (circular) movements. As a result, these two main groups of factors are identified as the most effective covariates that triggered landslides in the studied region. In addition, the most important human structures created in the region include villages (residential areas) and transportation roads, which, as may be observed in Figure 3, can directly be affected by changes in morphology. Thus, the topographic factor has been taken into account as the main triggering factor, and the others have been considered as triggering factors. This factor alone could not be enough to lead to landslide occurrence, but it is strongly believed to have had a significant effect on land movements in Gorzineh-khil. Topography also influences other parameters such as vegetation, wind, sunlight, and freezing, which are directly related to weathering. For this reason, this study has attempted to evaluate this stimulus as one of the basic triggering factors for the landslides in the studied area. It is necessary to state that, although geological, climatological, seismological, and human-related activities must be considered in comprehensive studies, application of the main triggering factors for the detection of the hazard potential of the region by using such machine-learning techniques could provide reliable results for landslide susceptibility assessment.