Leveraging Edge Computing for Real-Time Monitoring and Disaster Management in Flood-Prone Areas

Abstract

Floods can be dangerous to human lives, infrastructure, and economies, not to mention that in flood-prone regions, fast monitoring and disaster management are essential. Slowness in data processing and decision-making usually curbs the traditional flood monitoring processes. In this paper, the integration of edge computing with real-time flood monitoring systems is examined for use in disaster management in the region. The benefit of edge computing is that it brings computational capabilities nearer to the source of the data, which allows quicker processing of the data, reduces latency, and improves the efficiency of flood prediction and mitigation techniques. The offered system uses sensors, IoT, and edge computing platforms to gather and analyze the generated real-time data, and issue timely flood warnings, assisting the emergency reaction. The paper incorporates design and implementation of edge-based architecture, mathematical models on flood prediction, and data processing algorithms. Performance of the system is evaluated based on a number of different metrics, including prediction accuracy and response time, and compared with traditional methods. The results indicate that edge computing can significantly enhance the results in flood detection, early warning systems, and disaster management. The paper will help in the development of the field of flood management by providing a robust, scalable solution to monitor floods in flood-prone areas in real-time.