IOT-BASED FLOOD WARNNING SYSTEM TO ENHANCE COMMUNITY SA

Abstract

To improve community safety in urban areas prone to flash floods, this project creates an Internet of Things (IoT)-based Flood Warning System. The system continuously monitors water levels in drainage systems or flood-risk areas in real time by integrating float switch sensors with ESP32 microcontrollers. The system automatically triggers a multi-layered warning mechanism that notifies local residents and remote users when the water level reaches a predetermined threshold. These mechanisms include visual signals via indicator LEDs, audible signals via buzzers, and instant notifications via the Telegram app. This combination of hardware and software ensures fast and efficient warning delivery, allowing users enough time to take preventive actions. Hardware design, circuit integration, sensor calibration, embedded programming, and flood simulation testing are among the key phases of the development process that evaluate the reliability and performance of the system. With a fast response time of less than one second, the device demonstrated excellent accuracy in identifying three different water levels during testing. The reliability of communication between the ESP32 and the Telegram server was confirmed by the continuous and timely delivery of Telegram alerts. The system is suitable for widespread community deployments such as residential and urban drainage networks due to its small size, low power consumption and affordable price components. In summary, this Internet-based Flood Warning System has proven itself to be a reliable, affordable and easy-to-use solution for flood detection and early warning. In flood-prone areas, effective real-time monitoring and rapid response times can reduce the risk of property damage, loss of life and economic disruption. The system may be developed into a complete smart disaster management solution that promotes safer and more resilient communities with additional enhancements such as integration with meteorological data and mobile applications.