航空中一种安全关键复飞程序——基于机器学习集成不平衡学习的预测

IF 2.1 4区 地球科学 Q3 METEOROLOGY & ATMOSPHERIC SCIENCES
Afaq Khattak, Pak-wai Chan, Feng Chen, Haorong Peng, Caroline Mongina Matara
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引用次数: 0

摘要

由于低空风切变、倾盆大雨、跑道偏移和不稳定进近,飞机的最后进近阶段占全球所有航空事故的近一半。采用复飞(MAP)程序可以防止危险着陆,通常在这种情况下执行,但这是安全关键的,而且很少发生。本研究采用机器学习集成不平衡学习,基于环境和态势参数预测低水平风切变条件下的MAP。这些模型是使用2017-2021年香港国际机场(HKIA)试点报告(PIREP)开发的。最初,不平衡数据被应用于机器学习模型,如随机森林(RF)、光梯度增强机(LGBM)和极端梯度增强(XGBoost),但这些模型无法准确预测MAP的发生。然后,这些模型被用作集成不平衡学习方法的基本估计量,包括自步调集成(SPE)框架、平衡级联模型和易集成模型。使用XGboost作为基础估计器的SPE框架在召回率、F1分数、平衡精度和几何平均值方面比其他框架表现更好。然后,利用SHAP来解释SPE框架,XGboost作为基本估计器。结果表明,低空风切变强度、跑道方位和低空风切变的垂直位置对MAP的贡献最大。07C和07R跑道的MAP最多。大多数MAP是在低层风切变距离地面500英尺以内时启动的。强烈的顺风比逆风更能引发MAP。对于航空安全研究人员和机场管理部门来说,本研究提出的框架是一个有价值的工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Missed Approach, a Safety-Critical Go-Around Procedure in Aviation: Prediction Based on Machine Learning-Ensemble Imbalance Learning
The final approach phase of an aircraft accounts for nearly half of all aviation incidents worldwide due to low-level wind shear, heavy downpours, runway excursions, and unsteady approaches. Adopting the missed approach (MAP) procedures may prevent a risky landing, which is usually executed in those situations, but it is safety-critical and a rare occurrence. This study employed machine learning-ensemble imbalance learning to predict MAPs under low-level wind shear conditions based on environmental and situational parameters. The models were developed using the 2017–2021 Hong Kong International Airport (HKIA) Pilot Reports (PIREPs). Initially, imbalance data were applied to machine learning models such as the random forest (RF), light gradient boosting machine (LGBM), and extreme gradient boosting (XGBoost), but these were unable to accurately predict the occurrence of MAPs. Then, these models were used as base estimators for ensemble imbalance learning methods, including the self-paced ensemble (SPE) framework, the balance cascade model, and the easy ensemble model. The SPE framework utilizing XGboost as the base estimator performed better than other frameworks in terms of recall, F1-score, balanced accuracy, and geometric mean. Afterwards, SHAP was utilized to interpret the SPE framework with XGboost as the base estimator. Results showed that low-level wind shear magnitude, runway orientation, and vertical location of low-level wind shear contributed most to MAPs. Runways 07C and 07R had the most MAPs. Most MAPs were initiated when low-level wind shear was within 500 feet of the ground. Strong tailwind triggered MAPs more than headwind. For aviation safety researchers and airport authorities, the framework proposed in this study is a valuable tool.
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来源期刊
Advances in Meteorology
Advances in Meteorology 地学天文-气象与大气科学
CiteScore
5.30
自引率
3.40%
发文量
80
审稿时长
>12 weeks
期刊介绍: Advances in Meteorology is a peer-reviewed, Open Access journal that publishes original research articles as well as review articles in all areas of meteorology and climatology. Topics covered include, but are not limited to, forecasting techniques and applications, meteorological modeling, data analysis, atmospheric chemistry and physics, climate change, satellite meteorology, marine meteorology, and forest meteorology.
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