Real-Time Urban Flood Forecasting and Alert System

Urban flash floods have become a more frequent and destructive phenomenon, especially in rapidly urbanizing cities with inadequate drainage infrastructure. Traditional flood prediction systems are primarily designed for riverine flooding, which lacks the capability to address the localized and rapidly evolving nature of urban flash floods. This paper presents a real-time urban flood forecasting and alert system, which uses Internet of Things (IoT) sensors and machine learning (ML) algorithms to predict and mitigate urban flooding events. IoT sensors monitor key environmental parameters such as rainfall intensity, water levels, and drainage conditions in real time. Collected data is processed using predictive models like Random Forest and optimized Long Short-Term Memory networks that have been fine-tuned to handle time-series data. The system produces flood alerts with a reasonable lead time through the integration of forecasts with historical patterns of flooding. This approach is to make the urban flood resilience stronger and minimize damage to infrastructure while saving communities from the adverse effects of extreme weather events.