Detection of Turbulence Anomalies Using a Symbolic Classifier Algorithm in Airborne Quick Access Record(QAR)Data Analysis  

作　　者：Zibo ZHUANG Kunyun LIN Hongying ZHANG Pak-Wai CHAN 

机构地区：[1]Flight Academy,Civil Aviation University of China,Tianjin 300300,China [2]School of Electronic Information and Automation,Civil Aviation University of China,Tianjin 300300,China [3]Aviation Weather Services,Hong Kong Observatory,Hong Kong,China

出　　处：《Advances in Atmospheric Sciences》2024年第7期1438-1449,共12页大气科学进展（英文版）

基　　金：supported by the Meteorological Soft Science Project(Grant No.2023ZZXM29);the Natural Science Fund Project of Tianjin,China(Grant No.21JCYBJC00740);the Key Research and Development-Social Development Program of Jiangsu Province,China(Grant No.BE2021685).

摘　　要：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.

关 键 词：turbulence detection symbolic classifier quick access recorder data 

分 类 号：V321.225[航空宇航科学与技术—人机与环境工程] TP18[自动化与计算机技术—控制理论与控制工程]