## 5 Results
Conversely, dense vegetation cover can stabilize the soil, reducing the likelihood of landslides [44]. The construction of infrastructure can alter natural drainage and increase the risk of landslides. Excavation activities can destabilize slopes and increase the risk of landslides. The composition of the soil is fundamental. Geological features, such as faults or discontinuities, can affect ground stability. Areas with a history of tectonic activity are often at greater risk of landslides. Furthermore, the arrangement of geological layers in the slope can create conditions that favor the detachment of soil masses [45]. The lithological composition of the soil is crucial for its stability. Soils composed of inconsistent materials, such as sand and gravel, are more prone to landslides than solid rock ones. The presence of clay layers, which can absorb water and become slippery, is particularly concerning [46]. The data were transferred to the model after appropriate conversion, as outlined in the Methodology section. For model training and testing, the dataset was divided into 80 and 20 percent, respectively. The model determined a susceptibility index for each study area cell after completing the iterative cycle. KFold cross-validation was used to evaluate the model's performance. This technique involves dividing the dataset into (k) folds and training the random forest model on (k-1) folds while testing it on the remaining fold. This process is repeated for each fold to calculate evaluation metrics, and the averages are determined upon completion. After trying out different numbers of estimators, we found the model that performed the best. In the case in question, KFold(n_splits = 5) was used to split the dataset into five folds. The model was trained on four folds and validated on the remaining fold, repeating the process for each of the five folds. This ensured that every piece of data would be used for both training and validation. We used a confusion matrix to evaluate the model's performance, and from that, we obtained the evaluation metrics shown in Figure 8. The confusion matrix is a table that helps us see how the model's predictions match the actual values. True Positive (TP): the number of positive instances correctly classified as positive by the model;  
False Negative (FN): the number of positive cases incorrectly classified as negative by the model;  
False Positive (FP): the number of negative cases incorrectly classified as positive by the model;  
True Negative (TN): the number of negative cases correctly classified as negative by the model. To validate the reliability of the model, the following metrics were derived: accuracy, precision, recall, and F1 score. The results obtained by the five folds of cross-validation are outlined in Table 2.  
Based on these values, we can assume that the model is able to make predictions with high accuracy. Figure 8: Random forest confusion matrix. To verify the validity of the model, we made a comparison with another neural network, the self-normalizing neural network (SNN). We also obtained the confusion matrix (Figure 9) for this network, from which the relative metrics were obtained. The metrics obtained were as follows:  
Accuracy: 0.78;  
Precision: 0.76;  
Recall: 0.74;  
F1 score: 0.73. These metrics, compared to those obtained with the suggested model, highlight that the random forest model demonstrates superior performance compared to the SNN in all key indicators. In fact, in terms of the individual metrics, we made the following observations:  
1. Accuracy  
Accuracy measures the proportion of correct forecasts out of the total forecasts made. The random forest model achieves an impressive accuracy of 94.6%, correctly classifying most samples. In contrast, the SNN model has an accuracy of 78%, indicating that it makes more classification errors. 2. Precision  
Precision is defined as the proportion of true positives among all samples classified as positive.