Purahong, Boonchana

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Purahong, Boonchana
Alternative Name
Purahong, B.
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Email
boonchana.pu@kmitl.ac.th

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    Item type:Publication,
    Comparison of Support Vector Machine for Apron Allocation
    (2022-05-27)
    Kanjanasurat1, Isoon
    ;
    Purahong, Boonchana
    ;
    Teerapanpong, Saowaluk
    ;
    Benjangkaprasert, Chawalit
    This paper presents machine learning techniques for classifying parking stand locations in the apron allocation management service that affects total airport ground service processing time at airports where arriving aircraft land. SVM and Kernel SVM algorithms will be used, as well as Polynomial, Gaussian RBF, and Sigmoid, based on five input factors: aircraft identification, estimated time of arrival (ETOA), area of apron, type of aircraft, and target of stands. Then, we compared classification accuracy and performance using the Mean Absolute Error (MAE) and the squared mean error (Root Mean Square Error: RMSE), and discovered that the Gaussian RBF kernel of the SVM algorithm model is more accurate than the other model. This work may be beneficial in assisting airport's decision-makers and enhancing airport operations efficiency and predictability.
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    Landing Runway Assignment by Airport Traffic using Machine Learning
    (2022-05-27)
    Kanjanasurat1, Isoon
    ;
    Jungsuwadee, Wasarut
    ;
    Purahong, Boonchana
    ;
    Benjangkaprasert, Chawalit
    This paper presents the solutions to the overwhelming burden of air traffic controllers by reducing workload and optimizing runway capacity using machine learning tools to assign runways for incoming aircraft based on critical information such as aerodrome traffic information of aircraft taking off and landing on the runway at Suvarnabhumi Airport, THAILAND. The model is composed of four layers and three hidden layers. ReLU and Adam are the activation and optimization functions used in this model, respectively. The model was trained using assigned landing runway and traffic runway factors. Predicting the assigned runway is 82.77 percent accurate.