Identification of Airline Turbulence Using WOA-CatBoost Algorithm in Airborne Quick Access Record (QAR) Data

Author:

Zhuang Zibo1ORCID,Li Haosen2,Shao Jingyuan3,Chan Pak-Wai4ORCID,Tai Hongda1ORCID

Affiliation:

1. Institute of Aviation Meteorology, Civil Aviation University of China, Tianjin 300300, China

2. School of Safety Science and Engineering, Civil Aviation University of China, Tianjin 300300, China

3. Flight School, Civil Aviation University of China, Tianjin 300300, China

4. Aviation Weather Services, Hong Kong Observatory, Hongkong 999077, China

Abstract

Turbulence is a significant operational aviation safety hazard during all phases of flight. There is an urgent need for a method of airline turbulence identification in aviation systems to avoid turbulence hazards to aircraft during flight. Integrating flight data and machine learning significantly enhances the efficacy of turbulence identification. Nevertheless, present studies encounter issues including unstable model performance, challenges in data feature extraction, and parameter optimization. Hence, it is imperative to propose a superior approach to enhance the accuracy of turbulence identification along airline. The paper presents a combined swarm intelligence and machine learning model based on data mining for identifying airline turbulence. Based on the theory of swarm-intelligence-based optimization algorithm, the optimal parameters of Categorical Boosting (CatBoost) are obtained by introducing the whale optimization algorithm (WOA), and the corresponding WOA-CatBoost fusion model is established. Then, the Recursive Feature Elimination algorithm (RFE) is used to eliminate the data with lower feature weights, extract the effective features of the data, and the combination with the WOA brings robust optimization effects, whereby the accuracy of CatBoost increased by 11%. The WOA-CatBoost model can perform accurate turbulence identification from QAR data, comparable to that with established EDR approaches and outperforms traditional machine learning models. This discovery highlights the effectiveness of combining swarm intelligence and machine learning algorithms in turbulence monitoring systems to improve aviation safety.

Funder

the Natural Science Foundation of Tianjin Municipality, China

Meteorological Soft Science Project

Jiangsu Provincial Key Research and Development Program

Publisher

MDPI AG

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