An Airport Operation Assurance Risk Identification Model Based on the DBSCAN Algorithm

With the rapid growth of the aviation industry, airport operation assurance risk management has become a critical aspect of ensuring aviation safety. The Voluntary Reporting System (VRS) serves as a vital component of risk management, providing a wealth of textual data for risk identification. This paper proposes a text analysis approach based on the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm, capable of automatically identifying sources of hazards from voluntary reports, assessing the likelihood and consequence of risks, and further determining risk levels. An automated risk assessment model was constructed to improve the efficiency and safety of airport operation assurance. Additionally, the model’s output data can be integrated into the risk management processes that drive the digital twin airport as a mapping of physical entities in the information world.