## 7 Conclusions  
Flooding is occurring more frequently worldwide, making effective disaster risk management essential. This management should focus on reducing a territory's vulnerability and improving emergency responses. This study presents an innovative approach to identifying the areas most at risk of flooding using a machine learning system, with resultsvalidated through Markov chain analysis. The findings indicate that the characteristics of a territory significantly influence the occurrence of flooding. This study emphasizes how these technologies can enhance our understanding of the effects of climate variability and aid in the development of strategies for climate change mitigation and adaptation. The combination of machine learning and Markov chains provides a powerful tool for predicting and managing emergencies, potentially reducing economic damage and saving lives. This study presents a comprehensive comparison of the Random Forest (RF) model and the Long Short-Term Memory (LSTM) model, outlining the strengths and weaknesses of each. The RF model, in particular, demonstrated greater effectiveness in predictions within the specific context examined. Looking ahead, the study suggests a potential avenue for further research: the development of a combined RF and LSTM model that could harness the strengths of both approaches for improved prediction accuracy. The flood susceptibility map (FSM) plays a unique and crucial role in supporting decision-makers, as highlighted by this research. By identifying high-risk areas, the FSM facilitates informed decision-making in spatial planning. This enables the implementation of resilient design strategies and prioritizes mitigation efforts to enhance community resilience and reduce property damage during flood events. The FSM has multiple applications, serving as a guide for designing resilient infrastructure and significantly aiding the development of advanced and robust systems such as drainage systems, embankments, and flood barriers. These elements can play a crucial role in mitigating the impact of flooding. Additionally, the FSM supports land use planning by helping create plans to avoid construction in high-risk areas and promote development in safer locations. It also enhances emergency management, as improved flood forecasting allows for the rapid development of effective response plans, reduced evacuation times, and better coordination among relief agencies. One of the key components of this approach is the proactive analysis of the temporal dynamics of flood events, achieved through Markov chains. This method enables the simulation of the temporal evolution of flood risk, providing a dynamic perspective for monitoring and predicting risk conditions. Transitions between risk states (low, moderate, high, and very high) are modeled to quickly identify areas that may shift from low to high risk, thus improving the timeliness and effectiveness of emergency responses. Integrating real-time data with the predictive model ensures continuous updates of flood risk forecasts. This proactive approach enables authorities to make informed and timely decisions, enhancing asset management and emergency response efforts. Implementing an early warning system based on these predictions can significantly reduce economic damage and save lives. The findings lay a solid foundation for further research, which could focus on optimizing the model and integrating new data sources to improve risk management. Lastly, it is essential to emphasize that collaboration among various communities can facilitate the comparison of adopted strategies, allowing for the sharing of data and methodologies. This exchange can advance flood risk management and contribute to greater resilience in vulnerable communities. Conceptualization, V.B., L.B., G.B., G.M.M., and E.G. ; methodology, V.B., L.B., G.B., G.M.M., and E.G. ; software, V.B., L.B., G.B., G.M.M., and E.G. ; validation, V.B., L.B., G.B., G.M.M., and E.G. ; formal analysis, V.B., L.B., G.B., G.M.M., and E.G.