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Hybrid neural network models for forecasting ozone and particulate matter concentrations in the Republic of China

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Abstract

Ozone is a toxic gas with massive distinct chemical components from oxygen. Breathing ozone in the air can cause severe effects on human health, especially people who have asthma. It can cause long-lasting damage to the lungs and heart attacks and might lead to death. Forecasting the ozone concentration levels and related pollutant attribute is critical for developing sophisticated environment safety policies. In this paper, we present three artificial neural network (ANN) models to forecast the daily ozone (O3), coarse particulate matter (PM10), and particulate matter (PM2.5) concentrations in a highly polluted city in the Republic of China. The proposed models are (1) recurrent multilayer perceptron (RMLP), (2) recurrent fuzzy neural network (RFNN), and (3) hybridization of RFNN and grey wolf optimizer (GWO), which are referred to as RMLP-ANN, RFNN, and RFNN-GWO models, respectively. The performance of the proposed models is compared with other conventional models previously reported in the literature. The comparative results showed that the proposed models presented outstanding performance. The RFNN-GWO model revealed superior results in the modeling of O3, PM10, and PM2.5 compared with the RMLP-ANN and RFNN models.

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Authors and Affiliations

  1. Department of Computer Science, Al-Balqa Applied University, Al-Salt, Jordan

    Malik Braik

  2. Computer Science Department, Southern Connecticut State University, New Haven, CT, USA

    Alaa Sheta

  3. Department of Computer Information System, Al-Balqa Applied University, Al-Salt, Jordan

    Heba Al-Hiary

Authors
  1. Malik Braik
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  2. Alaa Sheta
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  3. Heba Al-Hiary
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Correspondence to Alaa Sheta.

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Braik, M., Sheta, A. & Al-Hiary, H. Hybrid neural network models for forecasting ozone and particulate matter concentrations in the Republic of China. Air Qual Atmos Health 13, 839–851 (2020). https://doi.org/10.1007/s11869-020-00841-7

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  • DOI: https://doi.org/10.1007/s11869-020-00841-7

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