An IoT-Enabled Early Warning Platform for Urban Flood-Risk Monitoring and Mitigation: The IN-FLOOD Case Study

Abstract

Urban drainage networks are increasingly exposed to critical operating conditions because urbanization limits rainfall infiltration while climate change intensifies extreme precipitation events. This paper presents an IoT-enabled early warning platform developed within the IN-FLOOD project and deployed in the Municipality of Cerisano, Southern Italy. The monitoring sites were selected through previous hydrodynamic analyses, which identified the most critical points of the urban drainage network. At each selected site, an ultrasonic sensor measures the distance from the water surface and enables the estimation of the conduit filling level. Sensor data is acquired by an ESP32-based field unit through Modbus RTU over RS-485. The measurements are then transmitted over a 4G connection using the MQTT publish/subscribe protocol. A centralized Django-based backend receives and validates the telemetry data before storing it and making it available to the warning services. Web and mobile applications provide infrastructure supervision and alert management, while also supporting notifications and georeferenced reports. The proposed platform translates numerical drainage-network analysis into a deployable municipal monitoring system that connects real-time hydraulic observation with operational response and citizen information.