A DEMATEL-ISM-BN Model for Causation Analysis of Safety Incidents in Civil Aviation Airport Flight Area

This study proposes a coupled causal analysis model integrating decision making trial and evaluation laboratory, interpretative structural modeling, and Bayesian network to address challenges in clarifying risk evolution paths and enabling dynamic prediction for airport flight area safety. A causal factor system for safety incidents is first constructed, covering human, equipment, environment, and management dimensions, based on aircraft incident reports, literature, and expert knowledge. The improved decision-making trial and evaluation laboratory method integrates objective accident causation chains with expert judgments to enhance relationship identification accuracy. The interpretative structural modeling then establishes a multi-level hierarchical structure, explicitly preserving critical cross-level relationships to better characterize system complexity. This optimized topology is mapped into a Bayesian network. Through improved methods for determining prior and conditional probabilities, the Bayesian parameter estimation enables the forward prediction of incident probabilities and the backward diagnosis of critical causation chains. A case study of an airport in North China demonstrates the model's effectiveness. The prior probability of a flight area safety incident is 4.26%. Under different evidence inputs, the probability dynamically ranges between 4.38% and 14.00%. Backward diagnosis identifies the key causation chain as: "imperfect departmental coordination mechanism → communication and coordination failure → inadequate emergency response → flight area safety incident". Comparisons with existing studies, such as questionnaire-based structural equation modeling, show the proposed model provides clearer path logic and advances from static correlation analysis to dynamic probabilistic inference. The case analysis results align closely with actual accident findings, validating the model's practical utility and reliability in proactive safety management for airport flight areas.