Causes and impact of extreme Low Level Wind Shear (LLWS) event at Soekarno-Hatta International Airport

Ratnasari, Y; Trilaksono, N J; Septiadi, Deni

doi:10.1088/1755-1315/1105/1/012011

Cited by 2 publications

(3 citation statements)

References 11 publications

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“…In regard to reliability, EBM has exhibited similar performance to advanced ML models [27][28][29]. Various airports across the globe have recorded numerous instances of SWS [7][8][9]. Prompt notifications of WS are of paramount significance due to the potential for SWS to negatively impact aircraft as well as airport operations.…”

Section: Introductionmentioning

confidence: 97%

“…Flight disruptions such as delays, cancellations, and catastrophes can transpire under particularly extreme circumstances [3][4][5][6]. Various airports across the globe have recorded numerous instances of SWS [7][8][9]. Prompt notifications of WS are of paramount significance due to the potential for SWS to negatively impact aircraft as well as airport operations.…”

Section: Introductionmentioning

confidence: 99%

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Explainable Boosting Machine: A Contemporary Glass-Box Strategy for the Assessment of Wind Shear Severity in the Runway Vicinity Based on the Doppler Light Detection and Ranging Data

Khattak,

Zhang,

Chan

et al. 2023

Atmosphere

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Pilots commonly undergo training to effectively manage instances of wind shear (WS) during both the landing and takeoff stages. Nevertheless, in exceptional circumstances, there may be instances of severe wind shear (SWS) surpassing a magnitude of 30 knots, leading to adverse effects on the operation of taking off and landing aircraft. This phenomenon can lead to the execution of aborted landing maneuvers and deviations from the intended glide path. This study utilized the explainable boosting machine (EBM), an advanced machine learning (ML) model known for its transparency, to predict the severity of WS occurrences and analyze the underlying factors. The dataset consisted of 21,392 data points from 2018 to 2022 acquired from two Doppler light detection and ranging (LiDAR) systems installed at Hong Kong International Airport (HKIA). Initially, the Doppler LiDAR data received data treatment in order to address the issue of data imbalance. Subsequently, utilizing the processed data, the hyperparameters of EBM were optimized using the Bayesian optimization technique. The EBM model underwent subsequent training and evaluation, wherein its performance metrics were computed and compared with those of an alternative glass-box model including decision tree (DT) and counterpart black-box models, namely, random forest (RF) and extreme gradient boosting (XGBoost). The EBM model trained on synthetic minority oversampling technique (SMOTE)-treated data demonstrated superior performance in comparison with the alternative models, as indicated by its higher geometric mean (0.77), balanced accuracy (0.78), and Matthews’ correlation coefficient (0.169). Furthermore, the EBM exhibited enhanced predictive performance and facilitated a comprehensive analysis of individual and pairwise factor interactions in the prediction of WS severity. This enabled the assessment of the factors that contributed to the instances of SWS in the proximity of airport runways.

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Section: Introductionmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

Explainable Boosting Machine: A Contemporary Glass-Box Strategy for the Assessment of Wind Shear Severity in the Runway Vicinity Based on the Doppler Light Detection and Ranging Data

Khattak,

Zhang,

Chan

et al. 2023

Atmosphere

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“…AWOS equipment presents and displays processed data ranging from 10-20 seconds resolution, per minute, per hour, or day. The results of the AWOS output are METAR, SPECI, and MET REPORT/SPECIAL [15,21]. In addition, AWOS can also provide a warning/alert if there is a significant change in weather elements.…”

Section: Introductionmentioning

confidence: 99%

The effect of the eruption of mount Hunga-Tonga on density altitude, air pressure and flight operations in eastern Indonesian

Ismanto,

Hidayati,

Setia Budi

2023

IOP Conf. Ser.: Earth Environ. Sci.

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There was an eruption of the Hunga Tonga volcano in the Pacific Ocean on January 15, 2022 and produced the phenomenon of Shock Waves in the atmosphere, in addition to sonic booms, and tsunami waves that spread to various parts of the world. Data from the automated weather observing system (AWOS) in several Airports in Indonesia were analyzed to assess the impact of the eruption of Mount Tonga on the Indonesian atmosphere. There are sudden changes in air pressure (QNH) at locations (Airports) including Sorong, Wamena, Ternate, Sumbawa, Biak, Geser, Lombok, Sabu, and Kupang, with a range between 0.6 to 1.9 Mb. These atmospheric waves propagate at a speed of about 1248 km/hour. This drastic change in air pressure is not followed by a significant change in density altitude which affects the effectiveness of the aircraft’s lift, but a sudden change will cause a change in air pressure (QNH) in the altimeter which can cause a change in aircraft altitude. This sudden change has high probability to result in a plane crash due to a stall or undershoot or collision with aircraft.

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Causes and impact of extreme Low Level Wind Shear (LLWS) event at Soekarno-Hatta International Airport

Cited by 2 publications

References 11 publications

Explainable Boosting Machine: A Contemporary Glass-Box Strategy for the Assessment of Wind Shear Severity in the Runway Vicinity Based on the Doppler Light Detection and Ranging Data

Explainable Boosting Machine: A Contemporary Glass-Box Strategy for the Assessment of Wind Shear Severity in the Runway Vicinity Based on the Doppler Light Detection and Ranging Data

The effect of the eruption of mount Hunga-Tonga on density altitude, air pressure and flight operations in eastern Indonesian

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