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

Abstract

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.

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Acknowledgements

The authors would like to acknowledge the Ministry of Higher Education Malaysia for providing a fundamental research grant scheme (No.: FRGS/1/2018/TK10/UNITEN/03/2). In addition, the authors would like to thank the Malaysian Meteorological Department (MetMalaysia) for providing the relevant data.

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Yafouz, A., Ahmed, A.N., Zaini, N. et al. Ozone Concentration Forecasting Based on Artificial Intelligence Techniques: A Systematic Review. Water Air Soil Pollut 232, 79 (2021). https://doi.org/10.1007/s11270-021-04989-5

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Keywords

  • Air quality
  • Ozone concentration prediction
  • Theoretic approaches
  • Machine learning and optimization algorithms