## 2 Materials and Methods
### Methods
#### 2.3.1 Analysis of Conditioning Factors  
In this research, 15 conditioning factors were applied [7; 12]. Table 2 shows some details of these factors. In the first stage, the relationship between landslides and the factors was studied by determining their density in each of the classes of factors considered, which made it possible to determine the conditions in which landslides originate [12]; among them, the most important are lithology of shales and sandy clays, crop areas, altitudes between 2700 and 3000 m, slopes lower than 20\({}^{\circ}\), high TWI values and proximity to rivers and roads. In a second stage, multicollinearity analysis was addressed by determining the Spearman correlation coefficient [7; 12] or the variance inflation factor (VIF) [7], which led to discarding the factors of solar radiation, SPI and STI, all correlated with each other and with slope, which was retained as a very important and influential factor. Finally, feature selection algorithms were applied using various methods (Classification and Regression Tree (CART), Random Forests, Boruta and Recursive Feature Elimination (RFE)), that determined the most important conditioning factors for the study area [7]: elevation, slope, TRI, lithology, NDVI and distance to roads. In this study, based on previous research, a new approach is proposed. It consists of dividing the set of conditioning factors into DEM-dependent and non-DEM-dependent factors, which is explained below. Although the main characteristics of the factors used have been described in previous research [7; 12], it is important to point out some specific aspects that reflect the relevance of these factors for this study. Dependent DEM Factors  
Elevation is a crucial factor in LSM, especially in mountainous regions. In low elevation ranges, it is associated with human activities and with the presence of loose materials that increase the possibility of landslide occurrence [29], however, high elevation ranges have a greater susceptibility to landslides [39]. The importance of this factor increases if combined with slope, orientation, curvature and lithology, among others [40]. Slope is important because it is related to vegetation growth due to the amount of sunlight it receives [29] and by the susceptibility to landslides themselves, depending on the degree of slope [41], it is also a critical factor in assessing and understanding landslide risk [42]. Orientation (aspect) is directly related to three characteristics that condition soil stability: (i) exposure to sunlight, (ii) moisture accumulation and (iii) vegetation growth [43]. Curvature is important because it is related to the direction of movement of the slides and also influences the speed of the slides [44], it also affects the intensity of soil erosion [29]. SPI is important because of its relationship to the strength of water flows and is an indicator of erosion potential [45]. TWI is an essential hydrological factor, especially in shallow landslides, as it indicates the \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Thematic Variable (Obtained From)** & **Conditioning Factor** & **Source** & **Scale/Resolution** \\ \hline Topographical (digital elevation model (DEM)) & \begin{tabular}{c} Aspect, Curvature, Elevation, Slope, \\ SPI, STI, TPI, TRI, TWI, Solar \\ radiation \\ \end{tabular} & SIGTIERRAS-IERSE & 3 m \\ \hline Land (Soil) Cover (Land cover map & \
\begin{tabular}{c} Land cover \\ NDVI \\ \end{tabular} & SIGTIERRAS &
\begin{tabular}{c} 1:25,000 \\ 30 cm \\ \end{tabular} \\ \cline{2-3}  & Distance to roads & IGM & 1:25,000 \\ \hline Hydrological (Rivers layer) & Distance to rivers & IGM & 1:25,000 \\ \hline Geo-lithological (Geological map) & Lithology & SNI & 1:100,000 \\ \hline SNI: Sistema Nacional de Informacion (Quito, Ecuador); IGM: Instituto Geografico Militar (Quito, Ecuador); IERSE: Instituto de Estudios de Regimen Secional de Ecuador (Universidad del Azuay). & \\ \end{tabular}
\end{table} 
Table 2: Conditioning factors utilized in this research.