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Hybrid LSTM-XGBoost Framework Predicts Landing-Phase Safety Events from Imbalanced Flight Data

This paper introduces an interpretable hybrid framework for predicting rare landing-phase safety events from imbalanced flight data. It fuses LSTM autoencoder reconstruction errors with statistical flight features and feeds them into a class-weighted XGBoost classifier. Evaluated on 53,750 NASA DASHlink approach windows, the method achieves mean macro recall above 0.90 across airports, with component comparisons and feature attribution analysis. The study offers practical guidance for adapting flight-data monitoring models to airport-level data availability.