Artificial Intelligence-driven early warning system for flood risk management in urban areas

Rapid urbanization and climate change are increasing the frequency and severity of floods, posing significant threats to both lives and infrastructure. To mitigate these risks, it is essential to enhance the alerting and communication mechanisms for pluvial flash floods, thereby improving community resilience and reducing losses.

This study proposes an effective, citizen-oriented Early Warning System (EWS) implemented and tested in the heritage city of Matera, Italy. This EWS aims to empower citizens by improving their understanding of local flood risks, enabling them to assess their personal exposure and the potential characteristics of floods that may affect them. This knowledge allows individuals to make informed decisions about when to act and which life-saving measures to take.

The system integrates Artificial Intelligence (AI) for flood monitoring, flood modeling, and risk communication.  Internet of Thing (IoT)-based cameras combined with deep learning algorithms, specifically the You Only Look Once (YOLO) model, estimate flood water depth and car submergence levels. Additionally, flood surface velocity can be computed using the Fudaa-LSPIV (Large-Scale Particle Image Velocimetry) method. A deep convolutional neural network (CNN) model has been developed for rapid and accurate real-time prediction of water depth and flow velocity of forecasted urban flash flood scenarios. The EWS includes threshold-based alerts concerning flood instability for pedestrians and vehicles, accompanied by signals and designed symbols for communicating risk and self-protection measures to enhance citizen resilience.

 Overall, the proposed citizen-oriented EWS is not intended to replace existing systems from competent authorities but to complement existing systems by fostering "flood literacy" among citizens. Furthermore, this research can assist municipal authorities in emergency management by providing reliable information about the timing of flood recession, which is crucial for prioritizing the accessibility of affected areas and determining which roads should be restored for traffic in the short term.