Embedded intelligent technology for real-time safety monitoring and early warning in tourism systems

Ensuring tourist safety in dynamic outdoor environments is critical, especially in regions prone to natural hazards such as wildfires, landslides, and flash floods. With global tourism growth, intelligent systems capable of real-time monitoring and early warning are increasingly necessary, as conventional approaches lack adaptability and predictive capability. This study proposes an integrated intelligent framework for tourism safety monitoring and early warning by combining ecological sensing, mobile data acquisition, and advanced machine learning. Real-time hazard data and anonymized tourist movement patterns are collected and pre-processed using Noise-Resistant Adaptive Normalisation (NRAN) to handle noise and missing values. Hierarchical Spatio-Temporal Encoding (HSTE) is applied for effective feature extraction. Hazard prediction is performed using a Binary Gannet Optimiser-driven Dynamic Random Forest Tree (BGO-DRFT), which adaptively responds to evolving environmental and visitor conditions. The system integrates IoT sensors, UAVs, and mobile applications for real-time alerts and response planning. Experimental results demonstrate improved accuracy, reduced false alarms, and faster response times compared to existing methods.