Integrating Machine Learning for Localized Forecasting System to Mitigate Flash Flood Events in the Municipality of Zagori

Climate change has intensified the severity and frequency of natural disasters, with rising global temperatures exacerbating droughts and intensifying rainfall, resulting in devastating flood events. The Municipality of Zagori, located in the Epirus region, Greece, experiences recurrent flash floods, particularly during the autumn and early winter months. To address this challenge, the SMILE project, funded by the Greek Government, aims to develop a localized forecasting system tailored to the specific needs of the Zagori Municipality, integrating machine learning techniques with traditional hydrological models.

This project proposes a comprehensive tool equipped with a monitoring system designed to provide real-time data on hydrometric and meteorological parameters. Leveraging machine learning algorithms, such as neural networks and ensemble methods, alongside traditional statistical and physical models, the SMILE system enhances the accuracy and reliability of weather and flood predictions for the Municipality of Zagori.

The SMILE system offers a user-friendly online platform, allowing stakeholders to access and process data from connected sensors, including hydrometric stations along torrents and meteorological stations across the watershed. Advanced feature engineering techniques are employed to extract meaningful information from large and diverse datasets, facilitating the development of robust prediction models.

Moreover, the system incorporates sensors connected to dataloggers with internal 4G modems, enabling real-time monitoring and interoperability with a 1D/2D hydraulic model. This hydraulic model, enhanced by machine learning insights, focuses on critical areas prone to flash floods, aiming to issue timely warnings and mitigate potential risks more effectively.

By integrating machine learning techniques with traditional hydrological models, the SMILE project seeks to enhance early warning capabilities and improve disaster preparedness in the Municipality of Zagori. The development of this localized forecasting system represents a proactive approach to address the impacts of climate change and mitigate the adverse effects of extreme weather events in vulnerable regions.