Air Quality Assessment Based on Landsat 8 Images Using Supervised Machine Learning Techniques

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dc.contributor.authorVijaranakul, Nattadet
dc.contributor.authorJaiyen, Saichon
dc.contributor.authorSrestasathiern, Panu
dc.contributor.authorLawawirojwong, Siam
dc.contributor.authorJitkajornwanich, Kulsawasd
dc.date.accessioned2026-08-06T10:28:41Z
dc.date.available2026-08-06T10:28:41Z
dc.date.issued2020-06-01
dc.description.abstractSince 2018 during the winter of every year (December - January), Thailand has been suffering from air pollution problems known as PM 2.5 toxic dust, affecting people's daily lives especially in Bangkok and its metroplex. To cope with this problem, one of the traditional methods used is to implement physical air quality measurement devices at specific locations. Currently there are 21 stations across Bangkok and surrounding areas. Each station can assess air quality at the station point along with the given radius, meaning that areas far away from the station will not be assessed properly. In this paper, we propose a methodology that incorporates satellite images for air quality assessment with supervised machine learning techniques. Several classification models tested in this paper are Decision Tree, Naïve Bayes, k-Nearest Neighbors (kNN), Random Forest, and Gradient Boosting. From our experiments, the highest performance model is Random Forest that has averaged accuracy of 0.914, averaged precision of 0.89, averaged recall of 0.814 and averaged F-1 score of 0.84825.
dc.identifier.citation17th International Conference on Electrical Engineering Electronics Computer Telecommunications and Information Technology Ecti Con 2020, 96-100, 2020
dc.identifier.doi10.1109/ECTI-CON49241.2020.9158333
dc.identifier.other2-s2.0-85091860227
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/10974
dc.source17th International Conference on Electrical Engineering Electronics Computer Telecommunications and Information Technology Ecti Con 2020
dc.subjectAir Quality Assessment
dc.subjectMachine Learning
dc.subjectPollution
dc.subjectSatellite Images
dc.titleAir Quality Assessment Based on Landsat 8 Images Using Supervised Machine Learning Techniques
dc.typeConference Paper

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