## 3 Materials and Methods  
In recent years, the increasing frequency and intensity of floods have highlighted the urgent need for more effective forecasting models. Research on flood monitoring and mapping has expanded, not to directly mitigate the impacts of land and urban changes, but rather to provide essential support for informed decision-making and targeted actions to address these challenges. Models have evolved over the past three decades. Time series models (TSMs) were dominant in flood prediction until 2010, while machine learning (ML) models, especially artificial neural networks (ANNs), have been the most dominant since 2011. Despite significant advancements, there remains potential for further integration of machine learning algorithms with other techniques to enhance predictive accuracy. This study contributes to this effort by introducing an innovative approach that combines Random Forest with Markov chains, leveraging LIDAR data and geospatial information to enhance flood risk prediction. Developed in Python 3.11.2, this model delivers high spatial accuracy, which is crucial for identifying vulnerable areas and guiding proactive interventions. Rather than acting as a direct solution to flood-related challenges, this system serves as a valuable resource for local authorities, empowering them to make informed land management and urban planning decisions to mitigate risks effectively. Figure 1 presents a graphical representation of the phases and data involved in flood risk assessment. Each process step, from data collection to modeling and validation, is outlined to emphasize the integrated and multidisciplinary approach adopted. This  
methodology considers various topographical and environmental factors influencing flooding, ensuring a more accurate and reliable risk assessment. The outcome of this process is the flood susceptibility map (FSM), which serves as a crucial tool for spatial flood risk assessment. It aids in identifying areas at risk and facilitates the planning of mitigation measures. The Random Forest model was chosen for its proven accuracy and robustness compared to other machine learning algorithms [29; 30]. While more advanced models, such as deep neural networks, exist, Random Forest strikes an optimal balance between interpretability, training speed, and predictive performance. This makes it particularly suitable for applications requiring a clear understanding of model decisions. Additionally, its lower susceptibility to overfitting enhances its reliability, making it a preferred choice for predictive analytics in complex environments. The integration of advanced technologies, such as machine learning and Markov chains, facilitates the identification of high-risk areas and supports more informed land management decisions. These tools enable the analysis of large volumes of historical and current data, helping to identify patterns and trends that contribute to more accurate flood forecasting. Ultimately, this approach aids in disaster mitigation and community protection. The Random Forest model introduces an element of randomness in selecting features for each tree, which helps reduce the correlation between trees and enhances the overall robustness of the model (Figure 2). The process can be broken down into the following phases:  
* Generating Subsets of Features: A random subset of features is selected from the original dataset for each node in a tree. This subset is much smaller than the available features, ensuring tree diversity. * Determining the Optimal Feature: Among the randomly selected features, the one that best optimizes data separation is identified using quality metrics such as entropy or the Gini index. Figure 1: Outline of flood risk analysis process. * Iterating the Process: This process is repeated for every node in every tree, ensuring that each tree is constructed using a unique set of features. This ensemble approach enhances the model's ability to generalize to unseen data.