arXiv cs.AI· Taha Asghar, Bilal Mufti, Syed Qasim Zaheer, Luqman Ahmed·· 11 小时前AI 评分20
混合机器学习预测航班着陆阶段不平衡安全事件
Hybrid Machine Learning for Imbalanced Landing-Phase Safety Event Prediction from Flight Data
AI 导读
研究提出一种可解释的混合特征融合框架，用 LSTM 自编码器刻画正常进近窗口的偏离程度，并将重建误差描述符与参数级统计飞行历史特征融合，输入类别加权 XGBoost 分类器区分 Normal、Speed High、Path High 和 Flaps Late 四类窗口。
正文 · 原文
Abstract: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.
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2611.08386 [cs.LG] |
| (or arXiv:2611.08386v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2611.08386 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Bilal Mufti [view email]
[v1]
Thu, 5 Nov 2026 18:04:26 UTC (484 KB)
来源：arXiv cs.AI · arxiv.org