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<p>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 &amp; 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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月14日

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  • Lu Wang,
  • Maoran Zhu,
  • Na Li

摘要

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.