"FLOODGUARD: An IoT-Powered Real-Time Flash Flood Monitoring and Forecasting System"

Abstract

Abstract: In recent times, the surge in flooding incidents has become a pressing global issue, inflicting extensive damage on both lives and livelihoods. While floods remain an inevitable force of nature, their devastating impact can be alleviated. With the aid of cutting-edge technologies like the Internet of Things (IoT), proactive flood forecasting has become increasingly feasible. Through the seamless integration of IoT data, populations can receive early warnings and evacuate to safety, safeguarding both lives and valuables. A real-time solution is imperative, leveraging IoT data streams to deliver timely flood alerts. Our study presents an IoT-driven prototype designed to collect hydrological data from rivers, encompassing metrics such as water flow, level, and discharge, alongside meteorological parameters like temperature, humidity, wind speed, and direction. Employing a Long Short-Term Memory (LSTM) model, we analyze and classify collected data based on water discharge, level, rainfall, and temperature, predicting flood events as "no alert," "yellow alert," "orange alert," or "red alert." Given the dynamic nature of rivers, accurately measuring total water discharge poses challenges. To address this, we propose a novel methodology utilizing water flow, sectional area width, and average depth. Furthermore, we tackle the complexities associated with accurately measuring rainfall due to erratic weather conditions. Our system demonstrates robust performance in predicting flood event states, achieving high F1-scores of 97% for "no alert," 97% for "yellow alert," 96% for "orange alert," and 98% for "red alert.