Flight Safety Risk Prediction for Civil Aircraft Approach and Landing

The approach and landing phase is the critical juncture in flight operations, where flight-state risks must be meticulously identified and mitigated to avert significant safety incidents. This paper proposes a risk prediction model that enhances the Light Gradient Boosting Machine (LightGBM) using the Whale Optimization Algorithm (WOA). First, a six-degree-of-freedom model is used to establish a system of differential equations to describe the aircraft’s flight states. In conjunction with longitudinal and lateral motion risk analyses, parameters prone to typical flight accidents are selected as risk prediction indicators, focusing on abnormal flight states. Subsequently, the LightGBM algorithm is employed to construct a flight safety risk prediction model using WOA to optimize hyperparameters within the LightGBM framework, enhancing the predictive accuracy of the model. Finally, a data set is created using actual flight data from a particular airline, aggregating flight-state parameter data from 910 flights for model training and testing. The proposed model’s performance is compared with similar machine-learning methods, including LightGBM, XGBoost, and Random Forest. The results demonstrate that the WOA–LightGBM model achieves 95.6% accuracy in predicting flight safety risks during the approach and landing phase, demonstrating a significant advantage in predictive precision over other methods.