Research on the Pre-Warning Method of Aircraft Long Landing Based on the XGboost Algorithm and Operation Characteristics Clustering

Abstract

Long landing hazardous events (long landings) are regarded as the most common unsafe events during an aircraft’s landing phase and are significantly influenced by pilots’ leveling operations. This paper proposes a pre-warning method for long aircraft landings based on operation characteristics clustering to better prevent the occurrence of long landing events and develop pre-warning technology for long aircraft landings applicable to actual civil aviation aircraft operations. Based on the quick access recorder (QAR) flight data of a Boeing B737-800 fleet, the Gaussian mixture model (GMM) clustering method was employed to cluster, group, analyze, and evaluate the pilot operation characteristics utilizing the relative indicators of aircraft speed in the takeoff and landing phases as the measurement indices. Moreover, a long landing pre-warning model was developed based on the eXtreme Gradient Boosting (XGBoost) algorithm to account for the overall characteristics of various operations. The complete accuracy, recall ratio, and precision of the long landing pre-warning method based on pilot operation characteristics clustering reached 89.66%, 89.16%, and 92.50%, respectively, in the test of the pre-warning model, demonstrating a significant improvement over those of the pre-warning model without considering the operation characteristics and presenting a more effective pre-warning effect. Optimizing the long landing pre-warning model with pilot operation characteristics can effectively improve the model’s pre-warning capabilities, assist the crew in making accurate decisions, and prevent unsafe events during aircraft landing.