A Machine Learning Application for Predicting and Alerting Missed Approaches for Airport Management

Chou, Chih-Sheng; Tien, Alex; Bateman, Hilton

doi:10.1109/dasc52595.2021.9594418

Cited by 6 publications

(3 citation statements)

References 5 publications

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“…Tey noticed that visibility, wind speed, and localizer deviation signifcantly impact MAP decision-making. Chou et al [9] used machine learning models to analyze the causes of MAPs. Tey observed that the categorical boosting model performed better than other models and that factors such as visibility, wind speed, and pressure were among the most important causes of MAPs.…”

Section: Introductionmentioning

confidence: 99%

“…Adjust ∆ j such that false positive rate of H j is ∇ pr(9) Discard all the instances from B that are classifed correctly by H j(10) j � T (11) Output: Single robust ensemble model: (12) H(x) � sgn(􏽐 T j�1 􏽐 k j l�1 ω j,l h j ,l (x) − 􏽐 T j�1 ∆ j ), Balance Cascade Approach. Input: Training dataset (x k , y k ) n 1 , hardness function (z), total number of bins (β), base estimator (ζ), the number of base estimators (∀), minority class in the training data (A), majority class in the training data (B) (2) Initialize: With the subsets of majority class (B ′ ) and minority class (A), train the base estimator (ζ) by utilizing random undersampling approach such that |B ′ | � |A| (3) for k � 1 to n do (4) Ensemble of the base estimators F k (x) � 1/k􏽐 k− 1 k�0 ζ k (x) (5) Te majority class dataset is separated into β bins with regards to z(x, y, F k ): (b 1 , b 2 , ..., b ξ ) (6) Te mean hardness contribution can be obtained in the any i th bin as…”

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confidence: 99%

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Missed Approach, a Safety-Critical Go-Around Procedure in Aviation: Prediction Based on Machine Learning-Ensemble Imbalance Learning

Khattak

Chan

Chen

et al. 2023

Advances in Meteorology

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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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Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

Missed Approach, a Safety-Critical Go-Around Procedure in Aviation: Prediction Based on Machine Learning-Ensemble Imbalance Learning

Khattak

Chan

Chen

et al. 2023

Advances in Meteorology

View full text Add to dashboard Cite

show abstract

“…In Figuet et al [9], the authors suggested a generalized additive model to predict the GA rate at Zurich Airport within the next hour by considering weather-related factors, traffic density information, aircraft and airline mix, etc. Moreover, Chou et al [16] investigated which supervised machine learning model can best estimate the GA rate for Denver Airport if the models are provided with information regarding the prevailing weather conditions, observed traffic density, and attributes of aircraft using the airport. By comparing 18 different machine learning models, the authors concluded that the best results can be achieved with the CatBoost algorithm.…”

Section: Introductionmentioning

confidence: 99%