A Hierarchical Quantitative Risk Assessment Framework for Evaluating Performance and Resilience in Drone-Assisted Systems

The rapid integration of UAVs (Unmanned Aerial Platforms) introduces new operational capabilities but also raises critical challenges. This paper presents a quantitative risk assessment approach for evaluating the risks related to drone-assisted systems. The methodology combines established standards with the principles of the multi-criteria hierarchy concept. First, a qualitative analysis is performed to identify and register the required risk elements. Following this, a hierarchical model is developed to model the dependencies between systems’ components, environmental factors, structural limitations, and operational uncertainties. An AHP-based (Analytic Hierarchy Process) process is applied to enable elements quantification. To demonstrate the applicability and feasibility of the proposed methodology, two different drone-assisted systems are examined, showcasing their effectiveness in evaluating critical risk elements and computing cumulative risk contribution to quantify and prioritize potential risk events. The results indicate the significance of the methodology in ranking the verified risk elements and identifying those that made the greatest contribution to system failure. As revealed, power- and weather-related elements are among the most significant contributors to performance deterioration. In addition, operator-related factors significantly contribute to the system’s overall functional performance, especially when it is manually controlled. Finally, a comparative analysis underscores the sensitivity of risk ranking to variations in AHP scoring.

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Publication Details

Journal
Drones
Published
2026-05-11
DOI
https://doi.org/10.3390/drones10050370
Primary Topic
Air Traffic Management and Optimization
Type
article
Field-Weighted Citation Impact
0.00
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article

A Hierarchical Quantitative Risk Assessment Framework for Evaluating Performance and Resilience in Drone-Assisted Systems

Konstantinos Katzis, Hamed Ahmadi, Stavros Katsaronas, Nektarios Fotiou
Drones
Air Traffic Management and Optimization
article

A Hierarchical Quantitative Risk Assessment Framework for Evaluating Performance and Resilience in Drone-Assisted Systems

Konstantinos Katzis, Hamed Ahmadi, Stavros Katsaronas, Nektarios Fotiou
article en

Abstract

The rapid integration of UAVs (Unmanned Aerial Platforms) introduces new operational capabilities but also raises critical challenges. This paper presents a quantitative risk assessment approach for evaluating the risks related to drone-assisted systems. The methodology combines established standards with the principles of the multi-criteria hierarchy concept. First, a qualitative analysis is performed to identify and register the required risk elements. Following this, a hierarchical model is developed to model the dependencies between systems’ components, environmental factors, structural limitations, and operational uncertainties. An AHP-based (Analytic Hierarchy Process) process is applied to enable elements quantification. To demonstrate the applicability and feasibility of the proposed methodology, two different drone-assisted systems are examined, showcasing their effectiveness in evaluating critical risk elements and computing cumulative risk contribution to quantify and prioritize potential risk events. The results indicate the significance of the methodology in ranking the verified risk elements and identifying those that made the greatest contribution to system failure. As revealed, power- and weather-related elements are among the most significant contributors to performance deterioration. In addition, operator-related factors significantly contribute to the system’s overall functional performance, especially when it is manually controlled. Finally, a comparative analysis underscores the sensitivity of risk ranking to variations in AHP scoring.

DronesVol. 10(5)
European University Cyprus (CY), University of York (GB)
Climate action
Openalex Percentile: Top 2%
Air Traffic Management and Optimization
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