AI-supported estimation of safety critical wind shear-induced aircraft go-around events utilizing pilot reports.

Autor:	Khattak A; Key Laboratory of Infrastructure Durability and Operation Safety in Airfield of CAAC, College of Transportation Engineering, Tongji University, 4800 Cao'an Road, Jiading, Shanghai, 201804, China., Zhang J; Second Research Institute of Civil Aviation Administration of China, Civil Unmanned Aircraft Traffic Management Key Laboratory of Sichuan Province, China., Chan PW; Hong Kong Observatory, 134A Nathan Road, Kowloon, Hong Kong, China., Chen F; Key Laboratory of Infrastructure Durability and Operation Safety in Airfield of CAAC, College of Transportation Engineering, Tongji University, 4800 Cao'an Road, Jiading, Shanghai, 201804, China., Matara CM; Department of Civil and Construction Engineering, University of Nairobi, P.O. Box 30197-00100, Nairobi, Kenya.
Jazyk:	angličtina
Zdroj:	Heliyon [Heliyon] 2024 Mar 21; Vol. 10 (7), pp. e28569. Date of Electronic Publication: 2024 Mar 21 (Print Publication: 2024).
DOI:	10.1016/j.heliyon.2024.e28569
Abstrakt:	The occurrence of wind shear and severe thunderstorms during the final approach phase contributes to nearly half of all aviation accidents. Pilots usually employ the go-around procedure in order to lower the likelihood of an unsafe landing. However, multiple factors influence the go-arounds induced by wind shear. In order to predict the wind shear-induced go-around, this study utilized a cutting-edge AI-based Combined Kernel and Tree Boosting (KTBoost) framework with various data augmentation strategies. First, the KTBoost model was trained, tested, and compared to other Machine Learning models using the data extracted from Hong Kong International Airport (HKIA)-based Pilot Reports for the years 2017-2021. The performance evaluation revealed that the KTBoost model with Synthetic Minority Oversampling Technique - Edited Nearest Neighbor (SMOTE-ENN)- augmented data demonstrated superior performance as measured by the F1-Score (94.37%) and G-Mean (94.87%). Subsequently, the SHapley Additive exPlanations (SHAP) approach was employed to elucidate the interpretation of the KTBoost model using data that had been treated with the SMOTE-ENN technique. According to the findings, flight type, wind shear magnitude, and approach runway contributed the most to the wind shear-induced go-around. Compared to international flights, Hong Kong-based airlines endured the highest number of wind shear-induced go-arounds. Shear due to the tailwind contributed more to the go-around than the headwinds. The runways with the most wind shear-induced Go-arounds were 07C and 07R. Competing Interests: The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Afaq Khattak reports financial support was provided by 10.13039/501100001809National Natural Science Foundation of China. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. (© 2024 The Authors.)
Databáze:	MEDLINE
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