Abstract

Natural disasters such as earthquakes, floods, and wildfires pose significant threats to life and property, underscoring the need for effective early warning systems. This study introduces a real-time disaster detection system that utilizes IoT sensors to monitor critical environmental parameters such as temperature, humidity, seismic activity, and air quality. The collected data is processed using advanced machine learning algorithms to identify anomalies and predict potential disasters. A cloud-based infrastructure facilitates seamless data transmission, real-time monitoring, and efficient decision-making. The system provides automated alerts to authorities and residents via mobile notifications, SMS, and sirens, enhancing disaster preparedness and minimizing damage. Experimental results confirm the system’s high accuracy and ability to significantly reduce response time compared to traditional methods. By integrating IoT and artificial intelligence, this solution offers improved disaster prediction and response capabilities. Future work will focus on enhancing sensor precision, expanding disaster scenarios, and adopting blockchain technology for secure and transparent data management. Refined Keywords