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CO concentration forecasting with attention-based LSTM and EMD for urban areas in Selangor

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dc.contributor.authorZaini N.en_US
dc.contributor.authorEan L.W.en_US
dc.contributor.authorAhmed A.N.en_US
dc.contributor.authorChow M.F.en_US
dc.contributor.authorMalek M.A.en_US
dc.contributor.authorid56905328500en_US
dc.contributor.authorid55324334700en_US
dc.contributor.authorid57214837520en_US
dc.contributor.authorid57214146115en_US
dc.contributor.authorid55636320055en_US
dc.date.accessioned2023-05-29T09:38:48Z
dc.date.available2023-05-29T09:38:48Z
dc.date.issued2022
dc.descriptionAir quality; Carbon monoxide; Deterioration; Forecasting; Health risks; Long short-term memory; Air pollutants; Air quality levels; Ambient air; Carbon monoxide concentration; Deep learning; Empirical Mode Decomposition; Forecasting and air quality; Heart disease; Human health; Urban areas; Empirical mode decompositionen_US
dc.description.abstractDeterioration of air quality levels due to the high concentration of air pollutants in ambient air, especially within urban areas, has severely affected human health. Constant exposure to a dangerous air pollutant of carbon monoxide (CO) may lead to serious health problems such as heart diseases, lung damage and respiratory system failure, which may increase mortality risk. Therefore, this study aims to forecast CO concentration in urban areas in Malaysia using a hybrid deep learning model. Empirical mode decomposition (EMD) is used to decompose CO concentration data into multiple components, namely intrinsic mode functions (IMFs) and a residual. Attention-based long short-term memory (ALSTM), the combination of multiple LSTM layers and an attention layer, is used to forecast the decomposed components individually. Then, the forecasted sub-sequences are accumulated to obtain the final forecasting of CO concentration. In this study, forecasting CO concentration is based on hourly historical time series data considering the effect of meteorological parameters. EMD-ALSTM outperforms individual LSTM and ALSTM models in terms of statistical evaluation analysis. The results indicate that the hybrid forecasting model has successfully forecasted CO concentration with reliable accuracy. � 2022 IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1109/ICDI57181.2022.10007246
dc.identifier.epage56
dc.identifier.scopus2-s2.0-85146994627
dc.identifier.spage53
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85146994627&doi=10.1109%2fICDI57181.2022.10007246&partnerID=40&md5=6709aa47a19a2754d7960e725647ff51
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/27026
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.sourceScopus
dc.sourcetitle2022 International Conference on Digital Transformation and Intelligence, ICDI 2022 - Proceedings
dc.titleCO concentration forecasting with attention-based LSTM and EMD for urban areas in Selangoren_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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