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Ozone Concentration Forecasting Based on Artificial Intelligence Techniques: A Systematic Review

Decision trees; Forecasting; Multilayer neural networks; Ozone; Predictive analytics; Support vector machines; Artificial intelligence techniques; Machine learning techniques; Multi layer perceptron; Optimization approach; Ozone concentration forecasting; Prediction accuracy; Stand-alone algorithm;...

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Main Authors: Yafouz A., Ahmed A.N., Zaini N., El-Shafie A.
Other Authors: 57221981418
Format: Review
Published: Springer Science and Business Media Deutschland GmbH 2023
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spelling my.uniten.dspace-263322023-05-29T17:09:13Z Ozone Concentration Forecasting Based on Artificial Intelligence Techniques: A Systematic Review Yafouz A. Ahmed A.N. Zaini N. El-Shafie A. 57221981418 57214837520 56905328500 16068189400 Decision trees; Forecasting; Multilayer neural networks; Ozone; Predictive analytics; Support vector machines; Artificial intelligence techniques; Machine learning techniques; Multi layer perceptron; Optimization approach; Ozone concentration forecasting; Prediction accuracy; Stand-alone algorithm; Tropospheric ozone concentration; Learning systems; ozone; air quality; algorithm; concentration (composition); machine learning; optimization; ozone; prediction; theoretical study; air pollutant; air quality; artificial intelligence; artificial neural network; concentration (parameter); decision tree; feed forward neural network; forecasting; fuzzy system; human; measurement accuracy; multilayer perceptron; prediction; random forest; recurrent neural network; Review; support vector machine; systematic review The prediction of tropospheric ozone concentrations is vital due to ozone�s passive impacts on atmosphere, people�s health, flora and fauna. However, ozone prediction is a complex process and the wide range of traditional models is incapable to obtain an accurate prediction. �Artificial intelligence�, �machine learning� and �ozone prediction model� search terms in the title, abstract or keywords are involved. Inclusion criteria include subject area (engineering, computer science), English language and being published from 2015. This criterion obtained 156 articles, which were categorized into 4 areas of interest based on the machine learning technique applied. Recently as a result of the rapid development in the technology and the increase in the number of measured data, artificial intelligence techniques have been intensively used in predicting ozone concentration as an alternative to the traditional models. Therefore, the main objective of this study is to investigate the most developed techniques that have been used in predicting ozone concentrations as well as theoretic approaches such as information set approaches, fuzzy set approach and probabilistic set approaches. It is clearly stated that the standalone algorithms such as decision tree (DT) and support vector machine (SVM) outperformed multilayer perceptron (MLP); however, the latter is massively implemented by many researchers in the prediction of ozone concentrations. This review paper investigated artificial intelligence techniques integrated with optimization approaches. It can be concluded that hybrid algorithms have significantly improved the prediction accuracy. However, the majority of the proposed hybrid models have limitations; thus, there is a need to develop better hybrid algorithm that is able to tackle all the drawbacks of the improved algorithms and capable to capture the ozone concentration changes with a high level of accuracy. � 2021, The Author(s), under exclusive licence to Springer Nature Switzerland AG part of Springer Nature. Final 2023-05-29T09:09:13Z 2023-05-29T09:09:13Z 2021 Review 10.1007/s11270-021-04989-5 2-s2.0-85100837466 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100837466&doi=10.1007%2fs11270-021-04989-5&partnerID=40&md5=e63e3354e1bcbbfcfa5c2e3765118b7e https://irepository.uniten.edu.my/handle/123456789/26332 232 2 79 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 Decision trees; Forecasting; Multilayer neural networks; Ozone; Predictive analytics; Support vector machines; Artificial intelligence techniques; Machine learning techniques; Multi layer perceptron; Optimization approach; Ozone concentration forecasting; Prediction accuracy; Stand-alone algorithm; Tropospheric ozone concentration; Learning systems; ozone; air quality; algorithm; concentration (composition); machine learning; optimization; ozone; prediction; theoretical study; air pollutant; air quality; artificial intelligence; artificial neural network; concentration (parameter); decision tree; feed forward neural network; forecasting; fuzzy system; human; measurement accuracy; multilayer perceptron; prediction; random forest; recurrent neural network; Review; support vector machine; systematic review
author2 57221981418
author_facet 57221981418
Yafouz A.
Ahmed A.N.
Zaini N.
El-Shafie A.
format Review
author Yafouz A.
Ahmed A.N.
Zaini N.
El-Shafie A.
spellingShingle Yafouz A.
Ahmed A.N.
Zaini N.
El-Shafie A.
Ozone Concentration Forecasting Based on Artificial Intelligence Techniques: A Systematic Review
author_sort Yafouz A.
title Ozone Concentration Forecasting Based on Artificial Intelligence Techniques: A Systematic Review
title_short Ozone Concentration Forecasting Based on Artificial Intelligence Techniques: A Systematic Review
title_full Ozone Concentration Forecasting Based on Artificial Intelligence Techniques: A Systematic Review
title_fullStr Ozone Concentration Forecasting Based on Artificial Intelligence Techniques: A Systematic Review
title_full_unstemmed Ozone Concentration Forecasting Based on Artificial Intelligence Techniques: A Systematic Review
title_sort ozone concentration forecasting based on artificial intelligence techniques: a systematic review
publisher Springer Science and Business Media Deutschland GmbH
publishDate 2023
_version_ 1806428368152821760
score 13.211869

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