Observations and numerical ­simulations of sea breezes at Hong Kong International Airport

Chan, Pak Wai; Hon, K. K.

doi:10.1002/wea.4282

Cited by 5 publications

(1 citation statement)

References 14 publications

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“…Te pilots' fnal approach might have been afected by tropical cyclones and southern monsoon winds. Tese results are in line with earlier research fndings as well [59][60][61][62].…”

Section: Factor Interactionsupporting

confidence: 92%

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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“…Te pilots' fnal approach might have been afected by tropical cyclones and southern monsoon winds. Tese results are in line with earlier research fndings as well [59][60][61][62].…”

Section: Factor Interactionsupporting

confidence: 92%

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

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Explainable Boosting Machine for Predicting Wind Shear-Induced Aircraft Go-around based on Pilot Reports

Khattak,

Chan,

Chen

et al. 2023

KSCE J Civ Eng

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Prediction and Interpretation of Low-Level Wind Shear Criticality Based on Its Altitude above Runway Level: Application of Bayesian Optimization–Ensemble Learning Classifiers and SHapley Additive exPlanations

et al. 2022

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Low-level wind shear (LLWS) is a rare occurrence and yet poses a major hazard to the safety of aircraft. LLWS event occurrence within 800 feet of the runway level are dangerous to approaching and departing aircraft and must be accurately predicted. In this study, first the Bayesian Optimization–Ensemble Learning Classifiers (BO-ELCs) including Adaptive Boosting, Light Gradient Boosting Machine, Categorical Boosting, Extreme Gradient Boosting, and Random Forest were trained and tested using a dataset of 234 LLWS events extracted from pilot flight reports (PIREPS) and weather reports at Hong Kong International Airport. Afterward, the SHapley Additive exPlanations (SHAP) algorithm was utilized to interpret the best BO-ELC. Based on the testing set, the results revealed that the Bayesian Optimization–Random Forest Classifier outperformed the other BO-ELCs in accuracy (0.714), F1-score (0.713), AUC-ROC (0.76), and AUR-PRC (0.75). The SHAP analysis found that the hourly temperature, wind speed, and runway 07LA were the top three crucial factors. A high hourly temperature and a moderate-to-high wind speed made Runway 07LA vulnerable to the occurrence of critical LLWS events. This research was a first attempt to forecast the criticality of LLWS in airport runway vicinities and will assist civil aviation airport authorities in making timely flight operation decisions.

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Observations and numerical simulations of sea breezes at Hong Kong International Airport

Cited by 5 publications

References 14 publications

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

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

Explainable Boosting Machine for Predicting Wind Shear-Induced Aircraft Go-around based on Pilot Reports

Prediction and Interpretation of Low-Level Wind Shear Criticality Based on Its Altitude above Runway Level: Application of Bayesian Optimization–Ensemble Learning Classifiers and SHapley Additive exPlanations

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Observations and numerical ­simulations of sea breezes at Hong Kong International Airport

Cited by 5 publications

References 14 publications

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

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

Explainable Boosting Machine for Predicting Wind Shear-Induced Aircraft Go-around based on Pilot Reports

Prediction and Interpretation of Low-Level Wind Shear Criticality Based on Its Altitude above Runway Level: Application of Bayesian Optimization–Ensemble Learning Classifiers and SHapley Additive exPlanations

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Observations and numerical simulations of sea breezes at Hong Kong International Airport