## 3 Materials and Methods
### Methodology
At the end of the iterative cycle, the identified susceptibility values are validated. The susceptibility index calculated by the ISPRA considers various factors, including geological and morphological variables, land slope, vegetation status, changes in land use over time and space, and rainfall data. These elements are essential to ensure that the modeling and simulation align with the methodologies employed by the ISPRA. The methodology is illustrated in Figure 4.  
This research considered several data types, including geological, lithological, historical rainfall, land use, and slope data. To transfer the data to the model, a vector conversion of the raster was required and involved the following steps:  
1. Create a Polygon Grid: Develop a polygon grid equivalent to the pixels of the raster to be converted;
2. Calculate Centroids: Determine the centroids of the polygons within the created grid;
3. Sample the Raster: Sample the raster that is being converted using the calculated centroids;
4. Copy Values: Transfer the values associated with the points in the polygon grid;
5. Save the Final Vector: Save the resulting complete vector. In particular, regarding phase 1, we start by converting the geometric component by creating a polygonal grid corresponding to the raster's pixels. This operation can be resource-intensive, depending on the size of the raster and the dimensions of its pixels. Similar to digital terrain models (DTMs), digital elevation models (DEMs), or digital surface models (DSMs), slope data also exist as a continuous variable raster. While creating the raster, we establish the following four parameters:  
1. Grid Type: We select a rectangular grid type, which will be square in this case, with a side length of 5 m. This aligns with the square pixels of the raster. The resulting vector grid should have essential characteristics and dimensions identical to those of the raster being converted. 2. Grid Extension: The grid extension must match the original raster, calculated from the starting layer. 3. Pixel Size: The pixel size is specified, which determines the dimensions of the basic rectangles in the resultant vector. 4. Reference System: The reference system is identified for the output vector. In phase 2, we calculate the centroids corresponding to the polygons in the created grid. During phase 3, we sample the raster to be converted. This involves working with the numerical values in the raster, which, in this case, represent slopes in degrees. Along with the centroid identifier for each pixel transformed into a polygon, the attribute table displays the corresponding slope value at that point. The final phase involves copying the values associated with the points in the polygon grid through a join operation. The attribute table must be transferred from the point grid to the polygon grid, effectively vectorizing the continuous slope raster. A spatial join facilitates the identification of pixel positions within the context of the studied area's land use, geology, and lithology. This join adds new fields to the data, enabling one-to-many relationships that clearly define the positions of the pixels and the centroids of the vector grid about land use, geological maps, and lithological compositions. Figure 4: Flowchart of the proposed methodology. The methodology outlined above provides a comprehensive framework for predicting landslide susceptibility using advanced machine learning techniques and geospatial analysis. The next section presents the results obtained from applying this methodology, highlighting the effectiveness and accuracy of the proposed model in identifying high-risk areas.