A hybrid fuzzy modeling framework based on decomposed fuzzy sets and Z-numbers for risk prioritization in air traffic safety with a real case application

Abstract

The rapid growth of global air traffic increases the complexity of airspace management, especially around risk management. To address related safety challenges, this study presents an integrated risk analysis model consists of Functional Hazard Analysis (FHA), Decomposed Fuzzy Sets (DFS), Z-numbers, and Fuzzy Inference System (FIS). The model systematically accounts for uncertainty in risk parameters and integrates confidences for experts' judgments. DFS assesses the consistency of experts' evaluations, while Z-numbers represent their reliability. Severity, probability, and detectability are evaluated within this fuzzy framework, and risks are classified by using IFS-based approach aligned with the ICAO risk matrix. The model is applied on 11 critical hazard scenarios from the Advanced Surface Movement Guidance and Control System (A-SMGCS) based on high traffic and low visibility. Results obtained confirm the model's ability to identify hazards and prioritize risks, offering a transparent, adaptable, and uncertainty-aware decision-support tool for aviation safety management.