Multi-level aircraft feature representation and selection for aviation environmental impact analysis

A wholesome understanding of aviation's environmental impacts is indispensable to the re-alization of a sustainable future of aviation. An accurate assessment of aircraft features that influence environmental impacts such as fuel burn, emissions, and noise is necessary to advance the current modeling capability and uncover the interactions between aircraft and their environmental impacts. Nevertheless, contemporary literature lacks a comprehensive feature selection study for aviation environmental impacts. This paper considers the problems of aircraft feature representation and selection for environmental impact analysis. First, aircraft features from different sources are integrated and filtered to obtain a comprehensive, yet essential feature set for representing an aircraft type. Then, a large-scale computer simulation is performed on over 200 unique aircraft types to obtain their environmental impacts during departure and arrival. Multiple supervised statistical learning approaches are utilized for the selection of critical aircraft features. In particular, single-and multi-target feature selections are performed on different levels of environmental impacts. The main results of this study include optimal subsets of features along with their relative importance scores for multiple levels of environmental impacts. A case study on an aircraft grouping and model substitution exercise shows that the improved aircraft representation using selected features and their importance information has the potential to better reflect an aircraft's characteristics on the environmental impacts. Benefits and potential limitations of the proposed methodology are thoroughly discussed in the paper.