Flood Monitoring System Using AI and IOT for Rural Area

Abstract

Floods are among the most damaging natural disasters, affecting lives, property, agriculture, and the economy. Due to climate change, urbanization, and poor drainage systems, flood occurrences are increasing. This project presents an intelligent flood monitoring and prediction system using Raspberry Pi, integrating sensors, IoT, and AI techniques. The system monitors environmental conditions such as water level and rainfall using sensors installed in flood-prone areas. Data is sent to a Raspberry Pi for processing. A camera module captures real-time images, which are analyzed using AI-based computer vision to detect rising water levels and overflow conditions. Combining sensor data with visual analysis improves accuracy and reduces false alarms. The system classifies flood conditions into levels such as safe, warning, danger, and extreme. It also uses AI models to predict floods based on past and current data. IoT cloud integration allows remote monitoring and data visualization. Alerts are automatically sent to users, ensuring quick response and safety. The system is cost-effective, scalable, and reliable. Floods are among the most harmful natural disasters, causing serious damage to human life, agriculture, infrastructure, and the economy. Due to climate change, unplanned urban growth, and poor drainage systems, flood events are increasing rapidly. Therefore, there is a strong need for smart and reliable monitoring systems. This project presents an intelligent flood monitoring and prediction system using Raspberry Pi, IoT, and Artificial Intelligence. The system collects real-time data such as water level and rainfall using sensors installed in flood-prone areas. A camera module is also used to capture live images, which are analyzed using AI-based computer vision to detect rising water levels and overflow situations. By combining sensor data and visual analysis, the system improves accuracy and reduces false alerts. It classifies flood conditions into different levels such as safe, warning, danger, and extreme. The system also uses AI models to predict floods in advance based on historical and real-time data. With cloud connectivity, users can monitor data remotely, and automatic alerts help people take timely action. Overall, this system is affordable, scalable, and useful for rural flood management.