## 3 Results
### Model Performance Analysis
In summary, the absence of significant divergences between the performances achieved on training (with SMOTE), cross-validation, and test sets, combined with the balanced distribution of errors and the consistency of the metrics, provided strong evidence that the model does not suffer from overfitting. In Table 4, it can be observed that the model accurately identified class 0 (non-landslide-prone areas), achieving strong classification performance. However, a slight decrease in performance was noted for class 1 (landslide-prone areas), as indicated by lower recall and F1-score values. This decrease suggested that the model was not fully capturing all landslide-prone areas, leading to the omission of some high-risk zones. Specifically, only 79% of landslide-prone areas were correctly recognized. Similarly, class imbalance was observed (with a support of 949,477 for class 0 and 196,004 for class 1), although it was less pronounced than what could be observed for flooded areas. The model achieved an accuracy of 92%, confirming that, overall, it performed well across all instances (1,145,481). \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{5}{c}{**Cross Validation**} \\ \hline
**No. of Folds** & **Precision** & **Recall** & **F1 Score** & **Support** \\ \hline
1 & 0.801 & 0.968 & 0.882 & 0.997 \\
2 & 0.815 & 0.966 & 0.884 & 0.997 \\
3 & 0.817 & 0.968 & 0.886 & 0.997 \\
4 & 0.803 & 0.968 & 0.878 & 0.997 \\
5 & 0.808 & 0.968 & 0.881 & 0.997 \\
6 & 0.819 & 0.967 & 0.887 & 0.997 \\
7 & 0.812 & 0.964 & 0.882 & 0.997 \\
8 & 0.803 & 0.968 & 0.878 & 0.997 \\
9 & 0.807 & 0.967 & 0.880 & 0.997 \\
10 & 0.808 & 0.965 & 0.879 & 0.997 \\ \(\upmu\) & 0.809 & 0.967 & 0.882 & 0.997 \\ Test sample & 0.837 & 0.958 & 0.893 & 0.997 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Cross-validation in 10-fold for flooded area (class 1). As shown in Figure 6, which was related to flood-prone areas, the model correctly classified many areas of the basin. Only a small number of areas were classified as either false-positive (0.06%) or false-negative (1.24%) flood-prone areas. This observation further reinforced the earlier discussion, confirming that the model exhibited excellent performance in accurately classifying flood-prone areas. Figure 7 shows the results obtained for landslide areas and, similarly, the model correctly classified the landslide areas. Only a few cases were false-positive (2.07%) and false-negative (6.48%) landslide areas, both in the upstream and downstream zones of the basin, following the recall and accuracy results obtained. Additionally, it is possible to assess the percentage contribution of each conditioning factor to the classification (Figure 8), which is crucial for understanding which parameters should be considered relevant in the classification of flood-prone and landslide-prone areas. The feature importance was reported based on the SHAP method. As illustrated in Figure 8, which refers to flood-prone areas, the SHAP analysis provided a clear overview of the relative importance and behavior of the conditioning factors. In Figure 8a, elevation emerged as the most influential variable, contributing to 50% of the model's predictions. Following this, relative elevation accounted for 17%, and drainage density contributed to 9%, leading to a cumulative importance of 76%. This threshold aligned with the general assumption that the most relevant features were those whose combined contribution reached at least 70-80%. These findings highlighted that flood susceptibility in the study area was predominantly influenced by terrain morphology and drainage structure. A closer look at the relative contributions (Figure 8a) revealed that, although relative elevation ranks second, its importance was not markedly higher than that of drainage density. This confirmed the dominance of elevation as the single most impactful factor in flood susceptibility mapping, with other variables contributing with moderate but significant influence.