## 5 Results
To verify the validity of the Random Forest model, standard performance metrics (e.g., accuracy, precision, recall) were calculated by comparing the predicted flood risk categories with the expected outcomes derived from the model's structure and input data. These expected outcomes are not based on historical flood event records, which are not available in a consistent and georeferenced form for the study area, but rather on the anticipated risk levels associated with specific combinations of environmental and meteorological conditions. The units of measurement used in the evaluation are consistent with the risk categories and reflect the degree of agreement between the model's predictions and the expected classification under given input conditions:  
The model achieved an RMSE of 0.118 and an MSE of 0.014, indicating a low average prediction error. The R\({}^{2}\) value of 0.80 suggests that the model explains 80% of the variance in the observed flood risk levels, confirming its strong predictive performance on the ordinal classification task. A sensitivity analysis was conducted to evaluate how the value of R2 changes with the number of variables in the Random Forest model and the results were shown in the following graph (Figure 11). The radar diagram (Figure 12) was also produced, which provides a clear and immediate view of the relative importance of variables in the model. Together, these diagrams provide a comprehensive understanding of the model's performance, both in terms of individual variable contributions and overall model improvement. Figure 12 illustrates the trend of model accuracy (measured by R\({}^{2}\)) as a function of the number of explanatory variables. This analysis is theoretical and simulates the progressive inclusion of additional variables, some of which are hypothetical and not explicitly listed in the manuscript, to evaluate the model's sensitivity to increasing complexity. The curve shows that accuracy improves with the addition of relevant variables up to a certain point (around eight variables), after which further additions lead to marginal gains or even slight decreases due to the potential introduction of noise or redundancy. It is important to note that this analysis is not based on data from individual monitoring stations, but on spatially distributed variables applied across the study area. The graph in Figure 11 represents a behavior in which the accuracy of the model (measured by R\({}^{2}\)) increases with the addition of relevant variables up to a certain point, after which the addition of additional variables does not lead to significant improvements and may even cause a slight decrease in accuracy due to the introduction of noise or irrelevant variables. The radar diagram (Figure 12), in addition to highlighting the relative importance of the variables, clearly showing which factors have the greatest impact on flood risk forecasts,shows the constancy of the R2 value considering all the variables together. It also suggests that the model is effective in predicting flood risk based on these variables. A flood susceptibility map (FSM) is a crucial tool for spatial flash flood risk assessment. This map was generated using the Random Forest (RF) model, which represents a systematic approach for flood risk assessment within the study area. The risks were classified into four distinct categories: \"low\", \"moderate\", \"high\", and \"very high\", which were associated with distinct colors to identify the level of risk (Figure 13). Figure 11: R\({}^{2}\) Sensitivity analysis. Figure 12: Variable radar. Figure 13 shows the spatial distribution of flood susceptibility across the study area, as predicted by the Random Forest model. The model assigns a risk level to each cell of a regular grid based on the values of multiple spatial predictors (e.g., slope, land use, precipitation, drainage density). These predictions are then visualized in QGIS, where the classified risk levels are mapped using a color scale.