LSTM 与 XGBoost 混合框架预测航班着陆阶段安全事件
Hybrid Machine Learning for Imbalanced Landing-Phase Safety Event Prediction from Flight Data
NASA DASHlink 数据上跑的飞机着陆异常检测，LSTM 自编码器加 XGBoost，召回率超过 0.9，做时序稀有事件检测的可以看看。
一项研究提出可解释的混合特征融合框架，用于多分类着陆阶段安全事件预测。LSTM 自编码器在正常飞行窗口上训练，用重构误差刻画偏离程度，再与统计飞行历史特征融合后输入类别加权 XGBoost，区分 Normal、Speed High、Path High、Flaps Late 四类窗口。评估基于 NASA DASHlink 的 53,750 个进近窗口，覆盖三个到达机场。在重复划分与重训练下，所有机场的 macro recall 平均超过 0.90。特征归因显示预测与垂直路径、构型、速度等运行相关变量关联。
Hybrid Machine Learning for Imbalanced Landing-Phase Safety Event Prediction from Flight Data
Landing-phase safety-event prediction requires the detection of rare abnormal approaches within strongly imbalanced multivariate flight data. Capturing coupled temporal behavior while distinguishing event-specific signatures across airport operating contexts remains a central modeling challenge. This study proposes an interpretable hybrid feature-fusion framework for multiclass landing-phase safety-event prediction. A long short-term memory (LSTM) autoencoder trained on nominal flight windows characterizes deviations from routine approach behavior. Its continuous reconstruction-error descriptor is fused with parameter-specific statistical flight-history features and supplied to a class-weighted XGBoost classifier to distinguish Normal, Speed High, Path High, and Flaps Late windows. The framework is evaluated using 53,750 NASA DASHlink approach windows from three arrival airports. Pooled and airport-specific learning strategies are compared alongside component-level benchmarks, repeated pipeline retraining, and feature-attribution analysis. The results demonstrate strong class-balanced detection, with mean macro recall above 0.90 across all airports under repeated partitioning and retraining. Component comparisons establish the importance of class weighting and show that continuous feature fusion outperforms reconstruction-error-only and hard-gate alternatives. Airport-level comparisons reveal benefits from local specialization at the two airports with larger datasets and from pooled training at the airport with the smallest dataset. Feature attributions associate predictions with operationally relevant vertical-path, configuration, speed, and approach-context variables. The framework provides an interpretable approach to rare-event detection and practical guidance for adapting flight-data monitoring models to airport-level data availability.