Data-Driven Long-Landing Event Detection and Interpretability Analysis in Civil Aviation

Abstract

Long-landing Events (LLEs) can occur as a result of pilot’s improper operation, resulting in shorter available runways and higher operating costs. The LLE can be effectively pinpointed by analyzing data from the Quick Access Recorder (QAR), which records all of the pilot’s operations during takeoff and landing. Traditionally, domain experts inspect LLEs by manually setting thresholds on uni-dimensional data. However, they cannot detect effectively the defects caused by the pilot’s maneuvering technique because the potential mutual information between different features in the large amount of data is not considered. This paper proposes a data-driven LLE detection and causation analysis workflow, which can automatically mine and analyze the mutual information, to overcome the existing problems. Firstly, a dataset is established based on the extracted QAR data from 2002 flights, considering the landing phase of the aircraft. Subsequently, this paper proposes a Hybrid Feature Selection (HFS) method for selecting features that are highly correlated with LLEs in both supervised and unsupervised ways. A categorical Light Gradient Boosting Machine (LGBM) with a Bayesian optimization (LGBMBO) model is used to determine the performance improvement. Furthermore, the model is visualized to analyze the marginal effect of key parameters for the LLEs by using SHapley Additive exPlanations (SHAP). The experimental results demonstrate that our model reduces computational cost and achieves better performance. Additionally, this paper demonstrates that LLEs can be avoided during the landing phase by maintaining the appropriate descent speed, aircraft altitude, and descent angle.

Keywords

• Flight safety
• long-landing event
• anomaly detection
• shapley additive explanations
• feature engineering
• machine learning