Revolutionizing Railway Disaster Management with Cutting-edge IoT Technologies and Decision Tree Algorithm

The article aims to provide a novel strategy that integrates cutting-edge Internet of Things technology to alter the way catastrophe management is handled in railway networks. The system being suggested uses a complex network of sensors and data analytics to identify, monitor, and react to a wide variety of incidents, natural catastrophes, and security concerns. Using real-time data from sensors like weather stations, seismic detectors, CCTV cameras, and drones, the system processes and analyzes this information using machine learning algorithms to find deviations from typical operating conditions. These sensors include weather stations, seismic detectors, CCTV cameras, and drones. The system immediately creates quick warnings and automates reaction mechanisms upon identifying possible catastrophes or dangers, increasing safety and decreasing the number of disturbances. In addition, the human-centered design gives railway authorities and first responders the ability to make educated judgments during times of emergency. In challenging environments, the system's robustness may be ensured by methods of ongoing improvement and redundancy, as well as resilience. This next-generation disaster resilience system powered by the Internet of Things is set to improve railway safety and resilience in the face of growing threats.