Abstract
As the risks associated with air turbulence are intensified by climate change and the growth of the aviation industry, it has become imperative to monitor and mitigate these threats to ensure civil aviation safety. The eddy dissipation rate (EDR) has been established as the standard metric for quantifying turbulence in civil aviation. This study aims to explore a universally applicable symbolic classification approach based on genetic programming to detect turbulence anomalies using quick access recorder (QAR) data. The detection of atmospheric turbulence is approached as an anomaly detection problem. Comparative evaluations demonstrate that this approach performs on par with direct EDR calculation methods in identifying turbulence events. Moreover, comparisons with alternative machine learning techniques indicate that the proposed technique is the optimal methodology currently available. In summary, the use of symbolic classification via genetic programming enables accurate turbulence detection from QAR data, comparable to that with established EDR approaches and surpassing that achieved with machine learning algorithms. This finding highlights the potential of integrating symbolic classifiers into turbulence monitoring systems to enhance civil aviation safety amidst rising environmental and operational hazards.
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Acknowledgements
This work was supported by the Meteorological Soft Science Project (Grant No. 2023ZZXM29), the Natural Science Fund Project of Tianjin, China (Grant No. 21JCYBJC00740), and the Key Research and Development-Social Development Program of Jiangsu Province, China (Grant No. BE2021685).
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Article Highlights
• Turbulence can be detected by our proposed method without the need to calculate or estimate the EDR.
• This study employs a symbolic classifier based on genetic programming to acquire knowledge and facilitate evolution from QAR data.
• Our trained model had an equivalent performance compared to the EDR on new flights.
This paper is a contribution to the special issue on AI Applications in Atmospheric and Oceanic Science: Pioneering the Future.
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Zhuang, Z., Lin, K., Zhang, H. et al. Detection of Turbulence Anomalies Using a Symbolic Classifier Algorithm in Airborne Quick Access Record (QAR) Data Analysis. Adv. Atmos. Sci. (2024). https://doi.org/10.1007/s00376-024-3195-x
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DOI: https://doi.org/10.1007/s00376-024-3195-x