Hybrid Decision Model Ranks Airspace Complexity for Drone Fire Patrols

Researchers in China have developed a hybrid decision-support framework that quantifies how complex a stretch of low-altitude airspace is, and they have tested it on the skies above Fuxin City, where unmanned aerial vehicles are used for forest fire patrol. The study, published in the International Journal of Aeronautical and Space Sciences by Hualing Bi, Wenxi Yang and Fuqiang Lu, addresses a stated gap in existing practice: according to the authors, current methods of airspace complexity assessment often lack tailored indicators and rely on simplistic weighting schemes, which limits their robustness.

The framework is an integrated multi-criteria decision analysis (MCDA) model that combines three established techniques. A fuzzy analytic hierarchy process (FAHP) captures expert judgment about the relative importance of different factors, while entropy weighting derives weights directly from the data itself, so the final assessment synthesizes subjective and objective weighting rather than depending on either input alone. The two weighting streams are then fed into an improved version of the TOPSIS ranking method, strengthened with grey relational analysis (GRA), which produces the final complexity ranking.

A central contribution is a comprehensive indicator system spanning four dimensions: safety, structure, operations and environment. Metrics include factors such as population density beneath the airspace and the complexity of the terrain. To model the traffic structure, the team generated a route network using an improved ant colony optimization algorithm.

The model was validated in a case study of forest fire patrol airspace over Fuxin City, in which seven grid regions were assessed. Region 6 emerged as the most complex, a result the researchers attribute to dense routes, high traffic and rugged terrain. The output is a quantitative complexity ranking across the seven regions, which successfully identified the high-risk sectors.

The authors position this ranking as a foundational decision-support tool for airspace managers: by highlighting which areas demand prioritized attention, the assessment can inform differentiated strategies for allocating resources. The study’s scope is a single case of seven grid regions in one Chinese city, applied to forest fire patrol operations, so the findings demonstrate the framework’s feasibility in that setting rather than universal performance across all low-altitude airspace.

Subject of Research: A hybrid multi-criteria decision analysis framework for assessing low-altitude airspace complexity, applied to UAV forest fire patrol over Fuxin City, China

Article Title: A Hybrid MCDA Framework for Low-Altitude Airspace Complexity Assessment with Application to Forest Fire Patrol

Article References: Bi, H., Yang, W., & Lu, F. (2026). A Hybrid MCDA Framework for Low-Altitude Airspace Complexity Assessment with Application to Forest Fire Patrol. International Journal of Aeronautical and Space Sciences. https://doi.org/10.1007/s42405-026-01321-5

Image Credits: AI Generated

DOI: 10.1007/s42405-026-01321-5

Keywords: low-altitude airspace, UAV operations, multi-criteria decision analysis, TOPSIS, entropy weighting, fuzzy analytic hierarchy process, grey relational analysis, ant colony optimization, forest fire patrol, airspace complexity, Fuxin City, Hybrid