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

air 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 Factors

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Bibliographic Details
Main Authors: Zaini N., Ean L.W., Ahmed A.N., Malek M.A.
Other Authors: 56905328500
Format: Review
Published: Springer Science and Business Media Deutschland GmbH 2023
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spelling my.uniten.dspace-272942023-05-29T17:42:15Z A systematic literature review of deep learning neural network for time series air quality forecasting Zaini N. Ean L.W. Ahmed A.N. Malek M.A. 56905328500 55324334700 57214837520 55636320055 air 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 Factors Rapid 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. Final 2023-05-29T09:42:14Z 2023-05-29T09:42:14Z 2022 Review 10.1007/s11356-021-17442-1 2-s2.0-85119892951 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119892951&doi=10.1007%2fs11356-021-17442-1&partnerID=40&md5=8420920861394db752b81d39e7fa0a64 https://irepository.uniten.edu.my/handle/123456789/27294 29 4 4958 4990 Springer Science and Business Media Deutschland GmbH Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
description air 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 Factors
author2 56905328500
author_facet 56905328500
Zaini N.
Ean L.W.
Ahmed A.N.
Malek M.A.
format Review
author Zaini N.
Ean L.W.
Ahmed A.N.
Malek M.A.
spellingShingle Zaini N.
Ean L.W.
Ahmed A.N.
Malek M.A.
A systematic literature review of deep learning neural network for time series air quality forecasting
author_sort Zaini N.
title A systematic literature review of deep learning neural network for time series air quality forecasting
title_short A systematic literature review of deep learning neural network for time series air quality forecasting
title_full A systematic literature review of deep learning neural network for time series air quality forecasting
title_fullStr A systematic literature review of deep learning neural network for time series air quality forecasting
title_full_unstemmed A systematic literature review of deep learning neural network for time series air quality forecasting
title_sort systematic literature review of deep learning neural network for time series air quality forecasting
publisher Springer Science and Business Media Deutschland GmbH
publishDate 2023
_version_ 1806424421354700800
score 13.223943

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