## 6 Discussion
### Comparison with Other Methodologies  
The findings underscore the importance of a modeling approach that not only achieves high predictive accuracy but also fosters strong interpretability and actionable insights. In this context, the comparison with other hydrogeological risk prediction methodologies demonstrates how the proposed model effectively balances precision, transparency, and flexibility. It successfully addresses the limitations of more complex approaches, such as LSTM, SVM, and CNN, as discussed below.  
In terms of performance, the proposed model achieves excellent results in both classification and regression metrics. Comparisons with algorithms commonly used in the literature further validate the effectiveness of this approach. For instance, while LSTM neural networks are designed to capture time dependencies in sequential data, they exhibit lower performance compared to our model. Similarly, Support Vector Machines (SVMs) and Convolutional Neural Networks (CNNs) show significant limitations.  
SVMs can be helpful in linear scenarios or when a well-chosen kernel is applied, but they struggle with heterogeneous and complex datasets, such as those arising from environmental and geospatial contexts. Furthermore, SVMs often lack interpretability, making it difficult to understand the contribution of individual variables to risk predictions. Studies, such as that by Granata et al. [42], have indicated that while SVMs can effectively model urban runoff, they require careful calibration and might not perform well in dynamic scenarios.  
On the other hand, CNNs excel in image processing but require substantial amounts of data and high computational resources. They are not inherently designed to model temporal components of risk unless paired with more complex architectures, such as CNN-LSTMs, which further complicate the system. A study by Wang et al. [41] utilized CNNs for flood susceptibility mapping; however, the results demonstrated lower interpretability and flexibility compared to tree-based models, such as Random Forest.  
In contrast, the proposed model maintains an optimal balance between accuracy, efficiency, and transparency. By utilizing LIDAR data to generate digital terrain models (DTMs), it ensures superior spatial accuracy. Additionally, stochastic modeling through Markov chains enables the simulation of risk evolution over time. This approach not only enhances the quality of forecasts but also serves as a valuable tool for spatial planning and emergency management.  
Even when compared to hybrid approaches present in the literature, such as the one proposed by Kim et al. [43], which combines LSTM and Random Forest for urban flood risk forecasting, the model stands out due to its ease of implementation and greater transparency in decision-making. While Kim's model necessitates complex numerical simulations to generate training data, the approach used here is grounded in observational and geospatial data, making it more replicable and adaptable to different geographical contexts.  
In summary, the proposed model is a robust, advanced, and adaptable solution that effectively overcomes the limitations of existing methodologies, providing a concrete contribution to flood risk management in the face of increasing climate vulnerability.