## 5 Results
With a precision of 93% for the random forest model, it correctly predicts a positive class 93% of the time. On the other hand, the SNN model has a precision of 76%, which suggests a higher likelihood of false positives--samples classified as positive that are actually not. 3. Recall  
Recall measures the proportion of true positives among all positive samples. The random forest model demonstrates a recall of 93.6%, meaning it successfully identifies \begin{table}
\begin{tabular}{c c c} \hline \hline
**Metrics** & **Mean** & **Standard Deviation** \\ \hline Accuracy & 0.946 & 0.011 \\ Precision & 0.93 & 0.013 \\ Recall & 0.936 & 0.011 \\ F1 score & 0.932 & 0.009 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Mean and standard deviation results. Figure 9: Self-normalizing neural network confusion matrix. 93.6% of positive samples. In comparison, the SNN model's recall is only 74%, which indicates it fails to identify a significant number of positive samples, resulting in a higher incidence of false negatives. 4. F1 Score  
The F1 score is the harmonic average of precision and recall, providing a balanced measure of a model's performance. The random forest model has an F1 score of 93.2%, reflecting a good balance between precision and recall. Conversely, the SNN model's F1 score of 73% suggests it needs help to maintain an effective balance between these two metrics. The best performances indicate that random forest is more reliable and accurate in predicting the susceptibility index, minimizing both false positives and false negatives. Additionally, we evaluated other essential aspects:  
* Robustness: Random forests are known for their robustness and are less prone to overfitting than models like neural networks. This characteristic may explain their superior performance in our analysis. * Interpretability: Random forests are more interpretable than neural networks, allowing for a better understanding of which features significantly influence predictions. * Training Time: Neural networks, such as SNNs, often require longer training times and greater computational resources than random forests. These factors lead us to conclude that random forest is an excellent choice for the susceptibility index prediction problem. A further element of the evaluation was the ROC curves obtained for the proposed model (Figure 10) and for the comparison network (Figure 11). Figure 11: Self-normalizing neural network’s ROC curve. Figure 10: Random forest’s ROC curve. In the graphs, we have three ROC curves for the different classes with their respective AUC (area under the curve) values. The classes are the most present in the classification obtained from the models, the meaning of which is given below. The RF curves indicate AUC values that exceed 0.90, while the SNN curves show values greater than 0.70. This suggests that the RF model is more effective at accurately identifying positive and negative classes without making errors. This further evaluation confirms that the proposed model has better predictive capabilities for assessing landslide susceptibility. At the end of the iterative cycle, the model determines a susceptibility value for each unit identified by its identification code. This value, reported in the last column of the original input file, is a key index that measures the likelihood of a given area experiencing instability phenomena, such as landslides or landslides, providing invaluable insights for our assessment. The susceptibility value is an integer that ranges from 0 to 6, with each number representing a different level of susceptibility:  
* 0: No susceptibility;
* 1: Low susceptibility;
* 2: Moderate susceptibility;
* 3: High susceptibility;
* 4: Very high susceptibility;
* 5: Extremely high susceptibility;
* 6: Maximum susceptibility. This scale, a crucial component of our assessment, should be interpreted to provide valuable insights into the necessary actions to be taken:  
* Low values (0-1) indicate areas with a low probability of instability.