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    Artificial Neural Network for Air Pollutant Concentration Predictions Based on Aircraft Trajectories over Suvarnabhumi International Airport
    (2025-04-01)
    Kamsing, Patcharin
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    Cao, Chunxiang
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    Boonpook, Wuttichai
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    Boonprong, Sornkitja
    ;
    Xu, Min
    Air pollutant concentration prediction is essential not only for effective air quality management but also for planning aircraft and ground vehicle route networks in terminal areas. In this work, an artificial neural network (ANN) is used to predict the concentration levels of four types of air pollutants (CO, NO<inf>2</inf>, PM<inf>2.5</inf>, and PM<inf>10</inf>) at Suvarnabhumi International Airport. By leveraging Automatic Dependent Surveillance-Broadcast (ADS-B) historical data, aircraft trajectory pattern clustering is implemented by using K-means and Gaussian mixture model (GMM) clustering algorithms. Then, those trajectory patterns are inputted together with other flight data into ANN computation processes, resulting in an effective air pollutant prediction model for each kind of focus pollutant. The results demonstrate that the mean square errors (MSEs) of the predicted models for CO and PM<inf>2.5</inf> have acceptable values of 51.7622 and 53.9682, respectively, while the predicted model for NO<inf>2</inf> and PM<inf>10</inf> has MSEs of 139.6674 and 124.2517, respectively. This study contributes to the advancement of air pollutant prediction methodologies, facilitating better decision-making processes, proactive air quality management, and route network planning at airports. Although some prediction models for focused air pollutants have slightly high MSEs, further study is needed to enhance the prediction model capacity.
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    Automatic Dependent Surveillance-Broadcast (ADS-B) data observation and quality assessment in Thailand
    (2021-06-27)
    Ninkaesorn, Waris
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    Poolgate, Sarut
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    Lunzear, Ittipat
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    Sophan, Somkit
    ;
    Phakphisut, Watid
    Automatic Dependent Surveillance-Broadcast (ADS-B) technology provides positions and other important parameters for aircraft during phases of flight. As ADS- B technology is required for all aircraft flying in the US as well as Europe, numerous countries are considering the similar adoption. However, its quality will need to be assessed in each region to ascertain the availability. Since King Mongkut's Institute of Technology Ladkrabang (KMITL) is located near the Suvarnabhumi International Airport, an ideal location for ADSB study, in this work, we develop an ADS-B aircraft tracking system using a software receiver on the Raspberry Pi board to obtain the raw ADS-B data and analyze its qualities. Preliminary results show that the Navigation Uncertainty Category position (NUCp) values are in the level 7 at 81.93% of entire messages.