AN IOT-DRIVEN SOLUTION FOR REAL-TIME FLASH FLOOD DETECTION AND FORECASTING

Floods are a frequent and devastating natural disaster, with India particularly vulnerable due to its varied geography and dense population. Despite flood mitigation efforts, significant losses persist, emphasizing the need for innovative solutions. This project proposes an IoT-based flood management system designed to improve flood forecasting, monitoring, and response in real time. The system consists of IoT-connected devices that collect key data, such as water levels, rainfall, and flow rates, from flood-prone areas. This data is transmitted to cloud platforms for reliable storage and analysis. Using predictive analytics, the system compares real-time data with historical trends to provide early warnings when thresholds are exceeded, enabling timely action by disaster management teams and notifying residents in affected areas. To address connectivity challenges during floods, the system uses cloud storage through platforms like ThingSpeak, ensuring uninterrupted access to real-time data. Machine learning and Artificial Neural Networks (ANN) are employed to analyze past events and predict flood severity, guiding authorities to take appropriate measures. The system’s adaptive nature improves over time, ensuring better responses to dynamic flood conditions. This IoT-based solution offers a scalable, cost-effective approach that enhances flood management in India, reducing damage and boosting community resilience.