AI-Based Flood Prediction and Alert System Using Machine Learning and Web Technologies

Abstract

Floods are among the most destructive natural disasters, causing significant loss of life, property damage, and environmental degradation worldwide. Early prediction and timely dissemination of alerts are crucial to minimizing the adverse impacts of such disasters. This project presents an AI-based Flood Prediction and Alert System that leverages machine learning techniques and web-based technologies to provide real-time flood risk assessment and location-based alert notifications.The system is designed using the Django web framework and integrates multiple components including user authentication, flood prediction modeling, alert management, and historical data tracking. Users can register and specify their geographic location, enabling the system to deliver personalized flood alerts based on city-specific conditions. Administrators can generate and broadcast alerts with varying severity levels such as low, medium, high, and critical.The core of the system is a machine learning model trained on environmental parameters such as rainfall, monthly rainfall, temperature, humidity, river level, and drainage capacity. These features are processed using a scaler and fed into a trained classification model to predict the likelihood of flood occurrence. The prediction results are categorized into risk levels and displayed to users through an intuitive dashboard.Additionally, the system maintains a history of user predictions, allowing for performance tracking and analysis. The admin dashboard provides insights into model performance metrics, user statistics, and alert history, enhancing decision-making capabilities. Error handling mechanisms ensure robustness, particularly in cases of model version mismatch or missing files.This solution aims to bridge the gap between predictive analytics and real-world disaster management by offering an accessible, scalable, and efficient platform. It can be extended with realtime data integration from weather APIs, IoT sensors, and satellite data for improved accuracy. The system contributes to smart city initiatives and disaster preparedness strategies by empowering both authorities and citizens with actionable insights.