Publication:
PM2.5 forecasting for an urban area based on deep learning and decomposition method

Simple item page
dc.contributor.authorZaini N.en_US
dc.contributor.authorEan L.W.en_US
dc.contributor.authorAhmed A.N.en_US
dc.contributor.authorAbdul Malek M.en_US
dc.contributor.authorChow M.F.en_US
dc.contributor.authorid56905328500en_US
dc.contributor.authorid55324334700en_US
dc.contributor.authorid57214837520en_US
dc.contributor.authorid57221404206en_US
dc.contributor.authorid57214146115en_US
dc.date.accessioned2023-05-29T09:36:01Z
dc.date.available2023-05-29T09:36:01Z
dc.date.issued2022
dc.descriptionair pollutant; air quality; article; deep learning; empirical mode decomposition; human; Malaysia; particulate matter; particulate matter 2.5; predictive model; short term memory; urban area; air pollutant; air pollution; forecasting; Air Pollutants; Air Pollution; Deep Learning; Forecasting; Humans; Neural Networks, Computer; Particulate Matteren_US
dc.description.abstractRapid growth in industrialization and urbanization have resulted in high concentration of air pollutants in the environment and thus causing severe air pollution. Excessive emission of particulate matter to ambient air has negatively impacted the health and well-being of human society. Therefore, accurate forecasting of air pollutant concentration is crucial to mitigate the associated health risk. This study aims to predict the hourly PM2.5 concentration for an urban area in Malaysia using a hybrid deep learning model. Ensemble empirical mode decomposition (EEMD) was employed to decompose the original sequence data of particulate matter into several subseries. Long short-term memory (LSTM) was used to individually forecast the decomposed subseries considering the influence of air pollutant parameters for 1-h ahead forecasting. Then, the outputs of each forecast were aggregated to obtain the final forecasting of PM2.5 concentration. This study utilized two air quality datasets from two monitoring stations to validate the performance of proposed hybrid EEMD-LSTM model based on various data distributions. The spatial and temporal correlation for the proposed dataset were analysed to determine the significant input parameters for the forecasting model. The LSTM architecture consists of two LSTM layers and the data decomposition method is added in the data pre-processing stage to improve the forecasting accuracy. Finally, a comparison analysis was conducted to compare the performance of the proposed model with other deep learning models. The results illustrated that EEMD-LSTM yielded the highest accuracy results among other deep learning models, and the hybrid forecasting model was proved to have superior performance as compared to individual models. � 2022, The Author(s).en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo17565
dc.identifier.doi10.1038/s41598-022-21769-1
dc.identifier.issue1
dc.identifier.scopus2-s2.0-85140233282
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85140233282&doi=10.1038%2fs41598-022-21769-1&partnerID=40&md5=d4550a44640e572b8c76528626f0ed40
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/26646
dc.identifier.volume12
dc.publisherNature Researchen_US
dc.relation.ispartofAll Open Access, Gold
dc.sourceScopus
dc.sourcetitleScientific Reports
dc.titlePM2.5 forecasting for an urban area based on deep learning and decomposition methoden_US
dc.typeArticleen_US
dspace.entity.typePublication
Files
Collections
SCOPUS