Research on the path of sustainable tourism development based on optical augmented reality and artificial intelligence decision support

Artificial Intelligence (AI) and optical technologies represent a transformative combination for addressing the challenges of sustainable tourism. This study proposes an AI-driven Decision Support System (DSS) enhanced with optical sensing and image processing technologies to optimize resource allocation, monitor environmental impacts, and improve decision-making. The system integrates advanced machine learning models—including supervised and unsupervised learning, anomaly detection, and optimization algorithms—with optical technologies such as satellite imaging, drone-based remote sensing, and spectral analysis. These tools enable the processing of large-scale, heterogeneous datasets collected from IoT sensors, optical imagery, and historical tourism records. Key contributions include the deployment of predictive modeling for visitor behavior forecasting, clustering algorithms for tourist segmentation, and optical anomaly detection for real-time environmental monitoring. Applied to a coastal tourism case study, the system demonstrated a 44% improvement in visitor flow forecasting accuracy, a 35% increase in environmental monitoring precision through optical data, and a 30% reduction in resource waste compared to traditional methods. This research highlights the ability of AI and optical technologies to dynamically adapt to changing tourism patterns and environmental conditions, providing actionable insights to policymakers and stakeholders. Future work will focus on enhanced scalability, the integration of unstructured data sources, and the incorporation of explainable AI for greater transparency and trust. The findings underscore the potential of computational and optical technologies to revolutionize sustainable tourism practices in complex and dynamic environments.