Abstract

Natural disasters such as earthquakes, floods, and wildfires have devastating consequences, necessitating efficient early warning systems. This paper presents a real-time disaster detection system leveraging IoT sensors to monitor environmental parameters, including temperature, humidity, seismic activity, and air quality. The system collects and processes sensor data using machine learning algorithms to detect anomalies and predict potential disasters. A cloud-based architecture ensures seamless data transmission and storage, enabling real-time monitoring and quick decision-making. The system issues automatic alerts to authorities and residents through mobile notifications, SMS, and sirens, enhancing preparedness and minimizing losses. Experimental results demonstrate the system's high accuracy in detecting disasters, significantly reducing response time compared to traditional methods. The integration of IoT with artificial intelligence improves disaster prediction capabilities, making it a reliable solution for mitigating risks. Future enhancements will focus on refining sensor accuracy, expanding disaster coverage, and incorporating blockchain for secure data handling.