## 5 Results  
The data used in our study consisted of rainfall data collected by ARPACAL over the past eleven years, from 2014 to 2024, lithological and land use data. Additionally, the LIDAR data relevant to the study area were sourced from the PST (Extraordinary Remote Sensing Plan) database [39]. This database, provided by the Ministry of the Environment and Energy Security (MASE), contains geospatial and earth observation information that can be utilized to monitor and predict environmental and territorial phenomena. Although there is no specific and universally recognized standard for selecting causal factors of floods that influence flood events [40], the interaction between various topographical and environmental factors plays a crucial role in flood risk assessment. The following factors were taken into account in the study:  
Rainfall Data: This provides the amount, intensity, and duration of rainfall. Heavy, prolonged rainfall can saturate soils and increase surface runoff, thereby heightening flood risk. Land Use: Urbanized areas with impermeable surfaces (e.g., roads, buildings) reduce water infiltration, while regions with natural vegetation absorb more water, lowering flood risk. Lithology Map: The composition and structure of subsurface rocks and sediments affect water absorption. Porous materials like sand absorb more water than compact ones like clay, influencing runoff behavior. LIDAR Data and DEMs: High-precision LIDAR data supplies detailed topographic information (elevation and slope) crucial for modeling water flow. Digital Elevation Models (DEMs) derived from LIDAR data help predict water accumulation and identify flood-prone areas. Two types of models are commonly used:  
DSMFirst: Captures the first return of the laser, including buildings, trees, and other surface objects. DSMLast: Captures the last return, focusing on the bare ground to produce a more accurate terrain representation. By removing surface features, a precise Digital Terrain Model (DTM) is obtained. These data are integrated to simulate floods: LIDAR files are loaded to create the DEM, features are extracted, and all the information feeds into the Random Forest-Markov chain forecasting model. This model analyzes the combined topographical and environmental factors to assess flood risk accurately. The feature importance values shown in Figure 8 are derived from the Random Forest model and are expressed as percentages. These values represent the relative contribution of each feature to the model's predictive performance, typically calculated using the mean decrease in impurity (Gini importance) across all decision trees in the ensemble. In this case, rainfall contributes approximately 35%, lithology 30%, elevation 25%, and land use 10% to the classification of flood risk. Along with feature importance plots, a sensitivity analysis was performed to assess how changes in input variables affect model predictions. The results of this analysis are illustrated in Figure 9, which displays the model's response to changes in key variables. While lithology and elevation are static variables, both rainfall and land use can vary over time and space. In particular, land use changes due to urbanization, deforestation, or agricultural practices can significantly alter surface runoff and infiltration capacity. Figure 9 reflects this distinction by showing simulated sensitivity curves for rainfall and land use, and theoretical curves for lithology and elevation. This approach enables us to capture both the dynamic and structural components of flood risk, providing a more realistic and comprehensive understanding of how each factor contributes to flood hazard. Sensitivity analysis is essential for understanding and managing flood risk. The figure illustrates how standardized changes in four key variables, Rainfall, Land Use, Lithology, and Elevation, affect predicted flood risk. Rainfall (solid blue line) and Land Use (dotted purple line) are treated as dynamic variables, with curves derived from model-based simulations.