## 3 Materials and Methods
### Dataset  
The flood risk forecasting model is based on four main categories of data: geospatial, environmental, meteorological, and LIDAR, offering an integrated view of the territory.  
* Geospatial Data and Land Use: The geological information comes from the Geological Map of Italy, with a focus on the Calabria Region, to assess soil permeability.
* Land use data comes from the CORINE Land Cover project, with a geometric accuracy of 25 m, and is critical for estimating the impact of urbanization on surface runoff. (LIDAR).
* Elevation and slope data are obtained from digital elevation models (DEMs) derived from LIDAR surveys, provided by the MASE Extraordinary Remote Sensing Plan (PST). With an elevation accuracy of less than 15 cm, this data makes it possible to identify depressed areas, slopes and water storage areas.
* Meteorological Data: Based on eleven years of observations, the rainfall data were obtained from ARPACAL (Agenzia Regionale per la Protezione dell'Ambiente della Calabria), which operates a network of ground-based rain gauges across the region. The spatial distribution of stations is denser in coastal and urban areas, with sparser coverage in mountainous zones. Data were accessed via formal request and processed in compliance with ARPACAL's data usage guidelines. Limitations include spatial gaps and occasional sensor outages, which were addressed through data cleaning and interpolation strategies as described above.  
The integration of meteorological, geological, LIDAR, and land use data was carried out through a methodological approach that leverages the distinct characteristics of each dataset. Meteorological data, collected daily over 11 years, represent the dynamic component of the model, capturing rainfall variability, one of the main drivers of flood events. This variability is modeled using Markov chains to simulate transitions between different risk states over time. In contrast, geological and LIDAR data provide a high-resolution, static representation of the territory's physical and morphological features, such as lithology, elevation, and slope. These datasets are fundamental for defining the intrinsic susceptibility of the territory to water accumulation and outflow. Land use data, while spatially detailed, are not strictly static. Changes in land cover due to urban expansion, deforestation, or agricultural transformation can significantly alter surface runoff and infiltration capacity over time. Therefore, land use is treated as a semi-dynamic variable, whose influence is modeled in combination with meteorological variability.  
The integration of these datasets occurs within the Random Forest model, which combines spatial information (e.g., lithology, elevation, land use) with dynamic meteorological data (precipitation) to classify areas into different flood risk categories. While lithology and elevation remain constant over the 11-year simulation period, land use and rainfall introduce variability--rainfall through monthly time series, and land use through its spatial heterogeneity and potential for change.  
This temporal component is further modeled using a Markov chain, which simulates the probabilistic evolution of flood risk over time based on the initial classifications provided by the Random Forest model.  
In summary, static data define the spatial predisposition to risk, while rainfall and land use dynamics drive temporal and spatial variability. This synergy enables the model to capture both structural and evolving aspects of flood risk, enhancing strategic understanding and forecasting capabilities.  
The results of the application of this methodology, presented in the following section, demonstrate the model's effectiveness and accuracy in identifying high-risk areas.