## Appendix C Predictive Model and Real-Time Data Integration  
The predictive model, as previously described, is a powerful tool that utilizes ma-chine learning techniques and Markov chains to effectively identify areas at risk by analyzing historical and geospatial data. The model's potential is vast, as it can be expanded into a system that acquires and analyzes real-time data to fully leverage its capabilities. This implementation is a significant step towards protecting communities and minimizing extreme events' economic and social impacts. By monitoring and predicting flood conditions in real-time, informed, and timely decisions can be made, enhancing emergency response and asset management. Achieving this requires a series of technical and operational actions outlined below, with the aim of creating a system that can promptly and effectively monitor and respond to flood conditions. To collect real-time data, it is essential to implement an Internet of Things (IoT) sensor network. These strategically placed sensors monitor meteorological variables such as rainfall, temperature, and humidity, as well as hydrological variables like river levels. The selection of installation sites should be based on a thorough analysis of at-risk areas and the geographical characteristics of the territory. Once the sensors are installed, ensuring a reliable communication network for transmitting the collected data to central servers is vital. Wi-Fi and cellular networks effectively guarantee continuous, uninterrupted data transmission. The central servers, configured using cloud technologies, must be able to receive, process, and store data in real-time, ensuring the system's scalability and reliability. The data pipeline is the central element of the real-time forecasting system. At this stage, sensor data must be cleaned and converted into usable formats. This process is efficiently managed using tools like Apache Kafka, which helps regulate the data flow. Once transformed, the data should be stored in a real-time database like Prometheus. This open-source toolkit is ideal for monitoring and alerting, as it is designed to handle large volumes of temporal data. This ensures that information is quickly and reliably accessible for future analysis. By implementing incremental learning techniques, the predictive model is always up-to-date with the latest information. These techniques, which allow the model to be up-dated with new data in real-time, are particularly well-suited for machine learning algorithms like Long-Short-Term Memory (LSTM) neural networks. These algorithms can adapt to changes in the data and continuously improve predictions. Furthermore, the integration of LSTMs with the Random Forest model used in the original framework offers significant benefits. This hybrid approach can substantially enhance predictive capabilities by leveraging the strengths of both models: LSTM's proficiency in handling temporal sequences and Random Forest's robustness. A crucial aspect of the real-time forecasting system is data visualization and early warning capabilities. Users can view flood risk forecasts in real-time by creating a dashboard, gaining a clear and immediate overview of current and future conditions. Tools such as Metabase or Grafana, known for their user-friendly interfaces and robust features, can be utilized to develop custom dashboards, ensuring a high-quality user experience. Regular simulations and continuous feedback from users and local authorities are essential for the real-time forecasting system to work effectively. Simulations must replicate different meteorological and hydrological conditions to evaluate system performance. Simulations help test the accuracy of the system and identify areas for improvement, while user feedback helps identify any issues and improve the usability of the system. Effective flood risk management requires predicting weather and hydrological conditions and implementing an early warning system that can send automatic notifications to both authorities and the public.