Explainable prediction for hard landing of civil aircraft based on LightGBM-SHAP
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.
万方数据
维普
