Interpretable ensemble imbalance learning strategies for the risk assessment of severe-low-level wind shear based on LiDAR and PIREPs

• Published in: Risk analysis Vol. 44; no. 5; pp. 1084 - 1102
• Main Authors: Khattak, Afaq, Chan, Pak-Wai, Chen, Feng, Peng, Haorong
• Format: Journal Article
• Language: English
• Published: United States Blackwell Publishing Ltd 01.05.2024
• Subjects: Air safety; Aircraft accidents & safety; Airports; Aviation; Data augmentation; Flight safety; Imbalance; Learning; Learning strategies; Lidar; Prediction models; Risk assessment; Risk factors; Runways; Safety; Wind; Wind shear; ensemble imbalance learning strategies; risk assessment; low‐level wind shear
• ISSN: 0272-4332; 1539-6924
• DOI: 10.1111/risa.14215

The occurrence of severe low-level wind shear (S-LLWS) events in the vicinity of airport runways poses a significant threat to flight safety and exacerbates a burgeoning problem in civil aviation. Identifying the risk factors that contribute to occurrences of S-LLWS can facilitate the improvement of aviation safety. Despite the significant influence of S-LLWS on aviation safety, its occurrence is relatively infrequent in comparison to non-SLLWS incidents. In this study, we develop an S-LLWS risk prediction model through the utilization of ensemble imbalance learning (EIL) strategies, namely, BalanceCascade, EasyEnsemble, and RUSBoost. The data for this study were obtained from PIREPs and LiDAR at Hong Kong International Airport. The analysis revealed that the BalanceCascade strategy outperforms EasyEnsemble and RUSBoost in terms of prediction performance. Afterward, the SHapley Additive exPlanations (SHAP) interpretation tool was used in conjunction with the BalanceCascade model for the risk assessment of various factors. The four most influential risk factors, according to the SHAP interpretation tool, were hourly temperature, runway 25LD, runway 25LA, and RWY (encounter location of LLWS). S-LLWS was likely to happen at Runway 25LD and Runway 25LA in temperatures ranging from low to moderate. Similarly, a high proportion of S-LLWS events occurred near the runway threshold, and a relatively small proportion occurred away from it. The EIL strategies in conjunction with the SHAP interpretation tool may accurately predict the S-LLWS without the need for data augmentation in the data pre-processing phase.