人工智能支持的安全关键风引发的飞机绕行事件的估计利用飞行员报告
Afaq Khattak1, Jianping Zhang2, Pak-Wai Chan3
1Key Laboratory of Infrastructure Durability and Operation Safety in Airfield of CAAC, College of Transportation Engineering, Tongji University, 4800 Cao'an Road, Jiading, Shanghai, 201804, China.
Heliyon
|April 1, 2024
概括
预测风引发的旋转对于航空安全至关重要. 一个基于AI的KTBoost模型,增强了SMOTE-ENN数据增强,准确地预测这些事件,识别了关键的贡献因素.
科学领域:
- 航空安全航空安全
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 最后接近时的风切割和严重的雷暴导致了近一半的航空事故.
- 飞行员使用绕行程序来减轻与不安全着陆相关的风险.
- 预测风引发的旋转需要了解多种影响因素.
研究的目的:
- 开发和评估基于人工智能的框架,用于预测风剪引发的旋转.
- 将KTBoost模型的性能与其他机器学习模型进行比较.
- 确定导致风引发的旋转的关键因素.
主要方法:
- 使用基于人工智能的综合内核和树增强 (KTBoost) 框架.
- 应用数据增强策略,包括合成少数群体过量采样技术 - 编辑最近邻居 (SMOTE-ENN).
- 使用香港国际机场 (2017-2021) 飞行员报告数据进行训练和测试模型,并使用SHapley添加式扩展 (SHAP) 进行解释.
主要成果:
- 采用SMOTE-ENN数据增强的KTBoost模型实现了卓越的性能 (F1-Score: 94.37%,G-Mean: 94.87%).
- 飞行类型,风剪幅度和接近跑道是风剪引发的旋转最重要的预测因素.
- 总部位于香港的航空公司比国际航班更多地经历了风力剪切引起的转机.
结论:
- 与 SMOTE-ENN 的 KTBoost 框架是预测风剪引发的回转的高效工具.
- 尾风剪切比对风剪切更大,是旋转的重要因素.
- 跑道07C和07R与风引发的转次数最多有关.
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