http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
- 中文 을 입력하시려면 zhongwen을 입력하시고 space를누르시면됩니다.
- 北京 을 입력하시려면 beijing을 입력하시고 space를 누르시면 됩니다.
RISS 인기검색어
- 검색키워드
- 저자 : Afaq Khattak (검색결과 2 건)
- 무료
- 기관 내 무료
- 유료
-
Afaq Khattak,Pakwai Chan,Feng Chen,Haorong Peng 대한토목학회 2023 KSCE Journal of Civil Engineering Vol.27 No.10
The go-around is a safety-critical procedure in civil aviation that is rarely executed but is essential to avoid risky landings. Analyzing the factors that trigger go-around events can aid in identifying measures that could lower their frequency. This involves circumstances that could be deemed abnormal and intrinsically harmful. The study employed the Explainable Boosting Machine (EBM), a contemporary transparent model, to predict aircraft go-arounds and interpret different influential factors. The model proposed exhibits comparable accuracy to black-box models. The study utilized pilot reports and applied SMOTE-ENN to address the imbalance problem. The EBM model was trained with treated data in conjunction with Bayesian optimization. The EBM model's performance was evaluated using holdout data and compared to binary logistic regression and decision tree models, as well as black-box models such as adaptive boosting, random forest, and extreme gradient boosting. The EBM model exhibited superior performance compared to other models in terms of precision (83.15%), recall (79.77%), geometric mean (77.29%), and Matthews’s correlation coefficient (0.453). The EBM algorithm enables the comprehensive interpretation of individual and pairwise factor interactions in predicting aircraft go-around outcomes from both global and local perspectives. This facilitates the assessment of the impact of different factors on go-around outcomes.
-
Khattak Afaq,Zhang Jianping,Chan Pak-wai,Chen Feng,Almujibah Hamad 한국기상학회 2024 Asia-Pacific Journal of Atmospheric Sciences Vol.60 No.3
The elevated occurrence rate of wind shear (WS) events near airport runways presents one of the major hazards to the safe and efficient operation of landing and takeoff procedures. As a consequence of this, aircraft are more likely to experience the possibility of losing control or encountering hindrances. Hence, it is crucial to assess the factors influencing wind shear occurrence. Previous studies extensively reported the susceptibility of the runways at Hong Kong International Airport (HKIA) to significant wind shear events. Therefore, in order to estimate WS magnitude near runways at HKIA and assess various contributing factors, this study presents a novel Local Cascade Ensemble (LCE) model with its hyperparameters optimized via a Tree-Structured Parzen Estimator (TPE) to estimate the wind shear magnitude. The pilot report data obtained from HKIA between 2017 and 2021 was employed for the training and evaluation of the TPE-tuned LCE model. The outcomes of the TPE-tuned LCE model were also compared to those of other contemporary machine learning (ML) models. The findings indicated that the TPE-tuned LCE model exhibited better predictive performance in comparison to other models, as assessed by a mean absolute error (MAE) of 4.38 knots, a mean squared error (MSE) of 70.28 knots, a root mean squared error (RMSE) of 8.38 knots, and coefficient of determination (R2) value of 0.79. Subsequently, model interpretation via SHapley Additive exPlanations (SHAP) technique was performed on the outcomes of TPE-tuned LCE. It indicated that that certain runways at HKIA, such as runway 07 C, 07 L, 25 C, and 25R, had a higher likelihood of experiencing elevated wind shear conditions within 1000 ft above the runway level.
연관 검색어 추천
이 검색어로 많이 본 자료
활용도 높은 자료
