IoT-enabled flood risk detection via dual-sensor water level monitoring and real-time alerting

Abstract

Flooding is a devastating natural disaster that causes significant loss of life and economic damage. Early detection and real-time alerts are essential for effective flood management, particularly in regions with challenging geographical conditions. This paper proposes an IoT-based flood monitoring system that leverages dual-sensor water level data for accurate risk prediction and timely alerts. A novel dual-sensor water level analysis algorithm is introduced to evaluate flood risks by analyzing the rate of water level increase and the differential between two sensor locations. The system integrates Node.js for backend processing and Google Maps API for spatial visualization, enabling real-time monitoring and alert dissemination. Experimental results demonstrate high accuracy of 94.66% in detecting flood risks and low latency of 10 seconds in generating alerts, proving the system's suitability for flood-prone areas like the Con River basin, Binh Dinh province.