## 3 Methodology
### Landslide Control Parameters  
In this study, the existing landslide inventories were merged. This resulted in a total of 257 slope instabilities with different volumes, ranging from one to hundreds of cubic meters. Each mapped slope instability was used to obtain relevant information for landslide control. The parameters used to control the landslide occurrence are shown in Table 1 and Figure 2. They are grouped into eighteen factors, which provide information about the terrain, location, and seismic parameters that contributed to the landslide triggering.  
Factors can be classified into two main categories: categorical and numerical variables. Categorical variables are used to represent different categories or groups of values, whereas numerical variables are used to quantitatively measure a property, either in a discrete or continuous form. Categorical variables represent qualitative information, such as lithology or terrain morphology, whereas numerical variables provide quantitative information, such as slope or roughness.  
It should be noted that some dummy variables were included as categorical variables. These variables are represented by binary numeric values, typically 0 and 1, and are used to encode categorical features in machine learning models. By converting a categorical variable into multiple dummy variables, a numerical representation that indicates the presence or absence of a specific category is created. Dummy variables are particularly useful in classification and regression algorithms where categorical features can have a significant impact on data prediction and analysis.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Type of Information** & **Slide Control Parameter** & **Variable** & **Variable Type** \\ \hline \multirow{7}{*}{Terrain} & Lithology & geologia & Carbonate rocks; quartzites and schists; conglomerates, sandstones, and clays; marks; alluvials \\  & & & Very deep canyons and ravines; mountain tops and high ridges; \\  & Morphology & landform & u-shaped valleys, flats, open slopes, high slopes, and plateaus; \\  & & & mid-slope streams and shallow valleys; channels in headwaters and \\  & Slope & pend & basin headwaters; mid-slope ridges and small mountains on plains \\  & Slope direction & pend & Continuous quantitative, measured in degrees. \\  & Space direction & orient & Continuous quantitative, measured in degrees. \\  & Concavity/convexity of & curvar & Continuous quantitative, measured in degrees. \\  & the terrain & concavity/convexity & pla & Continuous quantitative, measured in degrees. \\  & & perpendicular to the surface & pla & Continuous quantitative, measured in degrees. \\  & Concavity/convexity & & \\  & transverse to the surface & & \\  & Slope variation in the area & & \\  & occupied by the slope & & \\ \hline \multirow{7}{*}{Location} & Distance from slope to the & dist\_falla & Continuous quantitative, measured in km. \\  & & nearest fault plane & dist\_vai & Continuous quantitative, measured in km. \\  & Distance from slope to the & dist\_cauce & Continuous quantitative, measured in km. \\  & nearest water channel & & \\  & Distance from slope to & & \\  & the epicenter & & \\  & Altitude above sea level & z & \\ \hline \multirow{7}{*}{Earthquake} & Newmark displacement & sr08\_1 & Newmark displacement by [30] measured in cm. \\  & Critical acceleration & ac & Continuous quantitative, measured in g units (1 g = 9.81 m/s\({}^{2}\)). \\ \cline{1-1}  & Safety factor & fs & Continuous quantitative, dimensionless. \\ \cline{1-1}  & Arias Intensity & ia & Continuous quantitative, measured in m/s. \\ \cline{1-1}  & Peak Ground Acceleration & pga & Continuous quantitative, measured in g units (1 g = 9.81 m/s\({}^{2}\)). \\ \hline \end{tabular}
\end{table}
Table 1: Summary of control parameters used in the machine learning methods, classified by the type of information provided and according to their nature.