## 5 Results
Lithology (dashed green line) and Elevation (red dash-dot line) are considered static variables, and their curves represent theoretical relationships based on hydrological reasoning and their contribution to the model's classification. All curves are plotted on a uniform y-axis scale to enable the direct comparison of their influence on flood risk. These variables interact complexly, making an integrated analysis of all the essential factors in flood risk assessment. The assignment of weights to environmental factors in our model was determined through a sensitivity analysis, which identified the variables with the greatest impact on flood susceptibility. Rather than directly applying these weights, we employed Principal Component Analysis (PCA) to synthesize the information from the most influential variables into a reduced set of uncorrelated components. PCA allows for dimensionality reduction by eliminating redundancies and generating principal components that represent optimal linear combinations of the original variables. Figure 8: Feature importance. Figure 9: Sensitivity analysis of flood risk prediction for key variables. To classify flood susceptibility levels, we employed a synthetic index derived from the first principal component (PC1) of the PCA. The classification into four susceptibility classes (Low, Moderate, High, and Very High) was based on quartile thresholds of the PC1 scores. This statistical approach was adopted to ensure objectivity and comparability across spatial units in the absence of consistent hydrological benchmarks or flood incidence records. Importantly, this classification was used solely for spatial interpretation and visualization purposes, and not as a target variable in model training. PCA loadings were carefully interpreted to retain physical meaning, and a sensitivity analysis was conducted to assess the influence of individual variables (see Figure 9). The resulting susceptibility map represents a relative environmental predisposition to flooding, not an absolute hazard measure. Limitations of this approach are acknowledged, and future work will focus on calibrating the classification using hydrodynamic models, historical flood data, and, where feasible, ground-truthing. This approach makes it possible to identify the most vulnerable areas with objective criteria, avoiding distortions resulting from an arbitrary choice of classification parameters. Through this strategy, the model is able to integrate the relative importance of features with a robust aggregation method, improving the accuracy of flood risk prediction and facilitating the interpretation of results. The risk classes identified are as follows:  
* Low: R < T1;
* Moderate: T1\(\leq\)R<T2;
* High: T2\(\leq\)R<T3;
* Very High: R\(\geq\)T3. Risk class thresholds (quartile-based)  
Class | Risk index range  
Low | \(\leq\) \(-\)0.34  
Moderate | \(-\)0.34 to 0.01  
High | from 0.01 to 0.34  
Very High | > 0.34  
Negative values indicate environmental conditions that are less favorable to flooding than average. Positive values indicate more risk-prone conditions. The classification into quartiles does not imply that 50% of the territory is at absolute risk, but that in relation to the sample, half of the areas have more favorable conditions for flooding. The classification of flood susceptibility was performed independently from the model training phase. A synthetic index was computed for each area using a weighted combination of environmental variables (rainfall, lithology, land use, and altitude), based on their relative importance. This index was then divided into quartiles to identify areas with different levels of susceptibility: Low, Moderate, High, and Very High. This classification was not used as a target variable for model training. The Random Forest model was trained separately using 80% of the dataset, with meteorological data and environmental variables.