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A systematic literature review of deep learning neural network for time series air quality forecasting

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dc.citedby11
dc.contributor.authorZaini N.en_US
dc.contributor.authorEan L.W.en_US
dc.contributor.authorAhmed A.N.en_US
dc.contributor.authorMalek M.A.en_US
dc.contributor.authorid56905328500en_US
dc.contributor.authorid55324334700en_US
dc.contributor.authorid57214837520en_US
dc.contributor.authorid55636320055en_US
dc.date.accessioned2023-05-29T09:42:14Z
dc.date.available2023-05-29T09:42:14Z
dc.date.issued2022
dc.descriptionair quality; algorithm; artificial neural network; industrial development; literature review; machine learning; public health; time series; urbanization; air pollution; forecasting; human; time factor; Air Pollution; Deep Learning; Forecasting; Humans; Neural Networks, Computer; Time Factorsen_US
dc.description.abstractRapid progress of industrial development, urbanization and traffic has caused air quality reduction that negatively affects human health and environmental sustainability, especially among developed countries. Numerous studies on the development of air quality forecasting model using machine learning have been conducted to control air pollution. As such, there are significant numbers of reviews on the application of machine learning in air quality forecasting. Shallow architectures of machine learning exhibit several limitations and yield lower forecasting accuracy than deep learning architecture. Deep learning is a new technology in computational intelligence; thus, its application in air quality forecasting is still limited. This study aims to investigate the deep learning applications in time series air quality forecasting. Owing to this, literature search is conducted thoroughly from all scientific databases to avoid unnecessary clutter. This study summarizes and discusses different types of deep learning algorithms applied in air quality forecasting, including the theoretical backgrounds, hyperparameters, applications and limitations. Hybrid deep learning with data decomposition, optimization algorithm and spatiotemporal models are also presented to highlight those techniques� effectiveness in tackling the drawbacks of individual deep learning models. It is clearly stated that hybrid deep learning was able to forecast future air quality with higher accuracy than individual models. At the end of the study, some possible research directions are suggested for future model development. The main objective of this review study is to provide a comprehensive literature summary of deep learning applications in time series air quality forecasting that may benefit interested researchers for subsequent research. � 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1007/s11356-021-17442-1
dc.identifier.epage4990
dc.identifier.issue4
dc.identifier.scopus2-s2.0-85119892951
dc.identifier.spage4958
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85119892951&doi=10.1007%2fs11356-021-17442-1&partnerID=40&md5=8420920861394db752b81d39e7fa0a64
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/27294
dc.identifier.volume29
dc.publisherSpringer Science and Business Media Deutschland GmbHen_US
dc.sourceScopus
dc.sourcetitleEnvironmental Science and Pollution Research
dc.titleA systematic literature review of deep learning neural network for time series air quality forecastingen_US
dc.typeReviewen_US
dspace.entity.typePublication
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