Prediction of Multivariate Air Quality Time Series Data using Long Short-Term Memory Network

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dc.contributor.authorMohd Aftar Abu Bakara
dc.contributor.authorNoratiqah Mohd Ariff
dc.contributor.authorMohd Shahrul Mohd Nadzir
dc.contributor.authorOng Li Wen
dc.contributor.authorFatin Nur Afiqah Suris
dc.coverage.publicationMalaysia
dc.date.accessioned2024-05-10T03:25:37Z
dc.date.available2024-05-10T03:25:37Z
dc.date.issued2022
dc.description.abstractIn this study, the air quality model based on the Long Short-Term Memory Network (LSTM) and Auto-Regressive Integrated Moving Average (ARIMA) was developed. The prediction of the particulate matter 10 micrometers or less in diameter (PM10) in Malaysia could be made from both models, and their performance was compared. The purpose of comparison between the two models was to determine the most suitable model to use in predicting PM10 since it is the dominant pollutant in Malaysia most of the time, especially during the haze period. This study used air quality data obtained from the Department of Environment Malaysia from July 2017 to June 2019.
dc.identifier.citationBakar, M. A. A., Ariff, N. M., Nadzir, M. S. M., Wen, O. L., & Suris, F. N. A. (2022). Prediction of multivariate air quality time series data using long short-term memory network. Malaysian Journal of Fundamental and Applied Sciences, 18(1), 52-59.
dc.identifier.urihttps://repoemc.ukm.my/handle/123456789/591
dc.language.isoen
dc.publisherPenerbit UTM Press
dc.publisher.alternativeMalaysian Journal of Fundamental and Applied Sciences
dc.subjectAir quality
dc.subjectLong Short Memory Network (LSTM)
dc.subjectAuto-Regressive Integrated Moving Average (ARIMA)
dc.subjectforecasting model
dc.subjectmultivariate
dc.titlePrediction of Multivariate Air Quality Time Series Data using Long Short-Term Memory Network
dc.typeJournal

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