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基于LiDAR和PIREPs的可解释组合失衡学习策略,用于评估基于LiDAR和PIREPs的严重低水平风剪风的风险

Afaq Khattak¹, Pak-Wai Chan², Feng Chen¹

¹Key Laboratory of Infrastructure Durability and Operation Safety in Airfield of Civil Aviation Administration of China, College of Transportation Engineering, Tongji University, Jiading, Shanghai, China.

Risk analysis : an official publication of the Society for Risk Analysis

|September 13, 2023

概括

严重的低水平风力剪切 (S-LLWS) 事件威胁着飞行安全. 集体失衡学习模型准确预测S-LLWS风险,确定温度和跑道位置作为关键因素.

科学领域:

航空安全航空安全
气象学天气学
数据科学数据科学数据科学

背景情况:

在机场附近发生的严重低水平风切割 (S-LLWS) 事件对飞行安全构成重大风险.
了解S-LLWS风险因素对于提高航空安全至关重要.
在民用航空中,S-LLWS事件是罕见但至关重要的事件.

研究的目的:

使用整体失衡学习 (EIL) 策略开发S-LLWS风险的预测模型.
识别和评估导致S-LLWS发生的关键风险因素.
评估EIL策略和SHAP解释对S-LLWS预测的有效性.

主要方法:

使用的组合失衡学习 (EIL) 策略:BalanceCascade,EasyEnsemble和RUSBoost.
雇员使用香港国际机场的PIREP和LiDAR数据进行模型培训.
应用夏普利添加剂解释 (SHAP) 用于风险因素的解释.

主要成果:

与EasyEnsemble和RUSBoost相比,BalanceCascade策略的预测性能表现优越.
确定每小时的温度,25LD跑道,25LA跑道和碰撞位置 (RWY) 是最有影响力的风险因素.
在低至中等温度和接近跑道值 (跑道25LD和25LA) 时,S-LLWS事件更有可能发生.

结论:

EIL策略,特别是BalanceCascade,可以在没有数据增强的情况下准确预测S-LLWS.
SHAP分析有效地突出了S-LLWS的关键风险因素.
这些发现为加强有关S-LLWS的航空安全协议提供了宝贵的见解.

关键词:

整体失衡学习策略的学习策略低水平的风力剪切.风险评估风险评估风险评估

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