## 6 Discussion
### The Limitations, Scalability, and Adaptability of the Model  
The model was developed and validated using data from a single catchment, the Calopinace River basin in Reggio Calabria. This choice was guided by the availability of high-resolution LIDAR data, detailed land use and lithological maps, and a consistent 11-year meteorological dataset. The basin's complex topographic and urban characteristics provided a suitable testbed for methodological development.  
However, the use of a single basin limits the spatial generalizability of the results. The absence of spatial cross-validation across multiple watersheds or hydroclimatic regimes restricts the assessment of model robustness under varying environmental conditions.  
The current study is intended as a proof-of-concept to demonstrate the methodological soundness of the RF-Markov framework. Future research will focus on extending the model to additional catchments in southern Italy, characterized by diverse geomorphological and climatic features.  
Moreover, regional climate models, which use historical and current climate data to simulate future weather patterns, are essential for assessing the impacts of climate change at a local scale and for developing effective mitigation strategies.  
A limitation of the present study lies in the absence of georeferenced historical flood event data for the Calopinace River basin. While several major flood events have been documented (e.g., 1951, 1953, 1972, 2000, 2015), these records are primarily qualitative or aggregated at the municipal level, lacking the spatial resolution required for pixel-level validation. Consequently, a direct comparison between model predictions and historical flood extents was not feasible.  
To address this constraint, we adopted a proxy-based validation strategy grounded in real environmental predictors such as LIDAR-derived elevation, lithology, land use, and long-term rainfall averages and applied Principal Component Analysis (PCA) to derive a synthetic flood susceptibility index. This index, classified into four ordinal risk levels, served as a statistically robust and reproducible proxy for flood risk in the absence of empirical flood labels.  
The model's performance metrics (RMSE = 0.118, MSE = 0.014, R\({}^{2}\) = 0.80), obtained via 3-fold cross-validation, reflect its internal consistency and generalization capacity within the study area. While these metrics do not represent real-world predictive accuracy in the strictest sense, they provide a reliable internal benchmark given the available data.  
Despite this limitation, the model offers practical value for decision-makers operating in data-scarce environments. It provides a spatially explicit, interpretable, and reproducible framework for identifying areas of high environmental susceptibility to flooding, supporting proactive land use planning and early warning systems.  
Future work will focus on accessing and digitizing historical flood archives in collaboration with local authorities and civil protection agencies. This will enable more direct validation and the potential integration of event-based calibration techniques to further enhance model reliability. Finally, to further improve predictive capacity, combining the Random Forest algorithm with LSTM neural networks which excel at processing sequential and temporal data may capture long-term dependencies in climate and hydrological data. This combination promises to enhance flood risk forecasts, making the model more robust and adaptable to varying regional conditions.