## 2 Related Work  
Recent studies have increasingly focused on predicting flood risk due to the rising frequency and severity of extreme weather events, which cause significant economic damage and pose environmental concerns [20; 21]. Researchers have explored various methodologies to enhance prediction accuracy and support effective land management strategies.  
Traditionally, hydrological and statistical models have been widely used for flood prediction. However, these models often rely on simplified assumptions, such as linear relationships between variables, stationarity of processes, and limited spatial resolution. As a result, they struggle to capture the complex, nonlinear, and dynamic nature of flood phenomena, especially in heterogeneous environments. These limitations have prompted the adoption of more flexible and data-driven approaches, such as machine learning.  
Among these, Random Forest (RF) has emerged as a robust algorithm for flood risk assessment, particularly due to its ability to handle high-dimensional data, perform feature selection, and model nonlinear interactions. Zhu et al. [19] developed a deep learning approach integrating RF with transfer learning in flood prediction models. Theirmethodology utilizes RF to identify relevant flood characteristics and employs short-term long-memory (LSTM) neural networks to enhance temporal data processing.  
Roohi et al. [22] implemented RF alongside Sentinel-1 radar imagery to classify flood-affected areas, achieving high predictive accuracy. Their research demonstrates the advantages of integrating RF with geospatial data for flood susceptibility analysis.  
Kubendiran and Ramaiah [23] emphasized the role of remote sensing data (LIDAR, SAR) in improving flood mapping and prediction.  
Islam et al. [24] applied Markov chains to model temporal transitions in flood-prone areas, achieving high accuracy in short-term forecasting. However, Markov models alone are limited in capturing spatial heterogeneity and complex environmental interactions.  
Moreover, integrating LIDAR-derived Digital Elevation Models (DEMs) has significantly improved the accuracy of spatial analyses for flood risk mapping. Studies like that of Kader et al. [25] highlight the role of DEMs in refining land use analysis and flood susceptibility modeling.  
Moreover, the incorporation of high-resolution LIDAR-derived Digital Elevation Models (DEMs), lithological maps, land use data, and rainfall patterns further enhances the model's precision and contextual relevance. This multi-source integration enables us to overcome the limitations of traditional models that often rely on incomplete datasets.  
Recent works have explored hybrid approaches. Liu et al. [26] analyzed over 100 studies on the use of ML models (including RF, LSTM, XGBoost) for flood depth prediction, highlighting the potential of hybrid models. Ezziyyani et al. [27] presented a study exploring the use of CNN, SVM, and KNN for flood forecasting and management, with data from Sentinel-2 and weather. Xu et al. [28] focused their study on the use of LSTM for urban runoff prediction; useful for integrating the temporal component into predictive models.  
Our hybrid approach builds on these insights by combining the spatial modeling strengths of RF with the temporal forecasting capabilities of Markov chains. While RF has been effectively used in temporal prediction tasks, it does not natively model sequential state transitions. To address this, we integrated RF with Markov chains to simulate the temporal evolution of flood risk categories in a probabilistic framework.