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Explainable prediction for hard landing of civil aircraft based on LightGBM-SHAP
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Guosong XIAO1, 2, Jiachen LIU1, 3, Yuanshan ZHANG4, Lei DONG1, 2, **, Xi CHEN1, 2
China Safety Science Journal | 2024, 34(10) : 134 - 142
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China Safety Science Journal | 2024, 34(10): 134-142
• Safety engineering technology •
Explainable prediction for hard landing of civil aircraft based on LightGBM-SHAP
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Guosong XIAO1, 2, Jiachen LIU1, 3, Yuanshan ZHANG4, Lei DONG1, 2, **, Xi CHEN1, 2
Affiliations
  • 1 Key Laboratory of Civil Aircraft Airworthiness Technology，Civil Aviation University of China，Tianjin 300300，China
  • 2 Science and Technology Innovation Research Institute，Civil Aviation University of China，Tianjin 300300，China
  • 3 College of Safety Science and Engineering，Civil Aviation University of China，Tianjin 300300，China
  • 4 COMAC Flight Test Center，Shanghai 201323，China
Published: 2024-10-28 doi: 10.16265/j.cnki.issn1003-3033.2024.10.1123
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In order to prevent hard landing overrun events of civil aircraft，first，data including kinematics，system performance and other engineering parameters was collected from QAR. Then QAR data processing activities such as the airport segment clustering，sample balancing and statistical feature extraction were carried out. Subsequently，LightGBM model was used to predict the hard landing events of civil aircraft，and compared with extreme gradient boosting (XGBoost)，decision tree (DT) and long short-term memory (LSTM) models. Finally，the shapley additive explanation (SHAP) algorithm was employed to identify the causal mechanisms of hard landing events and to analyze the impact of various flight parameters on the model's prediction results. The result demonstrates that the proposed model not only exhibits high accuracy and precision in predicting hard landing events (accuracy，correctness and recall reaching 99%，92% and 88%，respectively)，but also provides quantitative and visual explanation information for the decision-making process of hard landing prediction for specific flight segments.

lightweight gradient boosting machine (LightGBM)  /  civil aircraft  /  hard landing  /  quick access recorder (QAR) data  /  machine learning  /  explainable
Guosong XIAO, Jiachen LIU, Yuanshan ZHANG, Lei DONG, Xi CHEN. Explainable prediction for hard landing of civil aircraft based on LightGBM-SHAP[J]. China Safety Science Journal, 2024 , 34 (10) : 134 -142 . DOI: 10.16265/j.cnki.issn1003-3033.2024.10.1123
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Article Info
doi: 10.16265/j.cnki.issn1003-3033.2024.10.1123
  • Receive Date：2024-06-10
  • Online Date：2025-07-09
  • Published：2024-10-28
Article Data
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History
  • Received：2024-06-10
  • Revised：2024-08-15
Funding
Affiliations
    1 Key Laboratory of Civil Aircraft Airworthiness Technology，Civil Aviation University of China，Tianjin 300300，China
    2 Science and Technology Innovation Research Institute，Civil Aviation University of China，Tianjin 300300，China
    3 College of Safety Science and Engineering，Civil Aviation University of China，Tianjin 300300，China
    4 COMAC Flight Test Center，Shanghai 201323，China
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