# Landslide Susceptibility Assessment by Using Convolutional Neural Network
## 3 Materials and Methods
### Model Data and Implementation  
This study provides a CNN-based model for investigating the landslide susceptibility of the Gorzineh-khil region. For susceptibility zonation, the triggering factors such as the topographic parameters (as the main triggering factors) and water conditions were categorized and exposed to the CNN algorithm that was implemented by the Python high-level programming language and conducted for landslide detection. Landslide-prone areas from the entire dataset were selected for the training (80%) and the tests (20%). In this regard, the topographic factor that includes slope curvature, topographical elevation, slope aspect, and weathering were considered the main susceptibility factors. Figure 7 presents the flowchart of the applied CNN method. The presented algorithm took into consideration the spatial dependency of the occurred landslides and surrounding areas based on neighborhood sensitivity to enter highly susceptible areas into the training dataset. As verification, the loss function for both the test sets and training sets was evaluated which indicated the amount of computational and processing error. The loss function is the function that computes the distance between the current output of the algorithm and the expected output. It's a method to evaluate how your algorithm models the data. On the other hand, by using the common classification algorithms in machine learning that include SVM, k-NN, and DT classifiers, the utilized CNN-based model was justified. Table 1 presents the relation between the applied triggering factors and landslide susceptibility conditions in the CNN-based methodology, which is implemented in the basic map (Figure 8). This map is used for all of the susceptibility assessment steps performed in this study and concluded by the 15 layers CNN-based algorithm. As observed in Figure 8, all 30 landslides (Table 2) that have occurred in the area and their surroundings represent historically high-susceptible zones with a spatial dependency of which neighboring pixels for preparing predictor variables were incorporated into the training dataset. These training points aided CNN's learning process to develop high-susceptible zones and these locations generated the pattern of high- to low-susceptible for landslide occurrence in the studied area. The historical landslide locations have been prepared based on MAJ and FRWMO reports. Table 2 presents additional information about the historical landslides used in this study as spatial dependency points for high-susceptible zones (landslide-prone areas). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Triggering** & **Class** & **Number of** & **Pixel** & **Landslide per** \\
**Factor** & **Class** & **Landslides** & **Intensity** & **Cent** \\ \hline \multirow{4}{*}{Slope curvature} & \(<\)10 & 0 & 207.738 & 7.84 \\  & 10–25 & 6 & 278.819 & 12.12 \\  & 25–50 & 7 & 323.206 & 18.20 \\  & 50–75 & 7 & 393.633 & 27.73 \\  & \(>\)75 & 10 & 277.958 & 34.11 \\ \hline \multirow{4}{*}{Topographical elevation} & 0–800 & 4 & 264.081 & 10.28 \\  & 800–1600 & 2 & 749.392 & 11.61 \\  & 1600–2400 & 8 & 435.844 & 21.81 \\  & 2400–3200 & 6 & 489.861 & 24.33 \\  & 3200–4000 & 10 & 392.303 & 31.97 \\ \hline \multirow{4}{*}{Slope aspect} & Flat & 0 & 21.630 & 0.00 \\  & North & 9 & 428.201 & 25.65 \\  & South & 7 & 482.767 & 19.84 \\  & East & 6 & 375.932 & 33.28 \\  & West & 8 & 441.835 & 21.23 \\ \hline \multirow{4}{*}{Weathering} & Fresh & 0 & 14.468 & 4.68 \\  & Low & 3 & 389.459 & 11.50 \\ \cline{1-1}  & Moderate & 7 & 370.655 & 28.38 \\ \cline{1-1}  & High & 12 & 497.019 & 33.35 \\ \cline{1-1}  & In-situ soil & 8 & 289.014 & 22.09 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Relations between the triggering factors and landslide susceptibility conditions. Figure 7: Flowchart of the CNN-based methodology.