A bayesian network approach for systemic risk analysis in unmanned aerial vehicle (UAV) operations.

Unmanned Aerial Vehicle (UAV) operations confront complex systemic risks that challenge traditional analytical methods. This paper develops a hierarchical Bayesian Network (BN) to quantitatively model these risks. Our model establishes causal pathways from foundational drivers to key performance indicators (KPIs): Safety, Mission Success, and Third-Party Risk. The baseline risk assessment reveals significant operational vulnerabilities. It identifies degraded pilot performance, evidenced by a 54% probability of ‘Poor’ Decision Making, as a primary contributor to a 56% baseline probability of an ‘Accident’. However, a comprehensive sensitivity analysis demonstrates a more critical insight: the operational environment, specifically ‘Adverse Weather’ and ‘Terrain & Obstacles’, constitutes the single most dominant risk driver across all KPIs. This finding underscores the strategic importance of rigorous pre-flight environmental assessment over in-flight reactive measures. Furthermore, the analysis reveals the necessity for differentiated mitigation strategies; Mission Success exhibits unique sensitivity to ‘Signal Interference’, a factor less critical for direct safety outcomes. This framework provides a data-driven, causal tool to support UAV operators in resource prioritization and systemic resilience enhancement within a complex operational landscape.