AI BASED REAL TIME APPROACH FOR EARLY DISASTER MANAGEMENT USING IOT ANALYTICS

This project proposes an advanced AI-driven IoT-based environmental monitoring system centered around the ESP32 microcontroller to enable early disaster detection and management in response to threats like floods, earthquake. It integrates GPS for location tracking, ultrasonic sensors for distance and liquid level measurement, vibration and accelerometer sensors for motion and structural monitoring, and flow and float sensors to track water dynamics, all transmitting real-time data wirelessly to a cloud platform via Wi-Fi for storage, analysis, and remote monitoring. The Blynk platform offers an intuitive interface accessible through smartphones and web browsers, enabling live data visualization, customizable alerts, and remote control, while AI-driven analytics enhance predictive capabilities, providing insights and anomaly detection for faster, proactive disaster responses. To improve accessibility, a dedicated website—built using Visual Studio Code—serves as an additional platform for displaying system data and providing control features. This comprehensive fusion of IoT technology, AI analytics, and user-friendly design forms an intelligent, scalable, and reliable solution for real-time disaster management, minimizing risks and enhancing safety and resilience. IoT devices gather live environmental data, which is then analyzed by AI to identify early disaster indicators, allowing quicker and more efficient response and management.