An Outlook of Ozone Air Pollution through Comparative Analysis of Artificial Neural Network, Regression, and Sensitivity ModelsAn Outlook of Ozone Air Pollution Through Comparative Analysis of Artificial Neural Network, Regression, and Sensitivity Models

  • J. S. Khan Department of Civil Engineering, Quaid e Awam University of Engineering Science and Technology, Sindh, Pakistan
  • S. Khoso Department of Civil Engineering, Quaid e Awam University of Engineering Science and Technology, Sindh, Pakistan
  • Z. Iqbal Faculty of Civil Engineering, Universiti Teknologi Malaysia, Malaysia
  • S. Sohu Department of Civil Engineering, Quaid-e-Awam University College of Engineering, Science & Technology, Pakistan
  • M. A. Keerio Civil Engineering Department, Quaid-e-Awam University of Engineering, Science & Technology, Nawabshah, Pakistan
Keywords: ozone pollution, environment, sustainability

Abstract

Air pollution and atmospheric ozone can cause damages to human health and to the environment. This study explores the potential approach of the artificial neural network (ANN) model and compares it with a regression model for predicting ozone concentration using different parameters and functions measured by the Climate Prediction Center of US National Weather Service. In addition, this study has compared the economic viability of ANN and other measuring methods. Results showed that the ANN-based model exhibited better performance. Such model types can be beneficial to government agencies. By predicting ozone concentration government agencies can take preventive measures to avoid significant health effects, protect local populations, and help preserve a sustainable environment.

Author Biographies

J. S. Khan, Department of Civil Engineering, Quaid e Awam University of Engineering Science and Technology, Sindh, Pakistan

Department of Civil Engineering, Quaid e Awam University of Engineering Science and Technology, Sakrand Road, Nawabshah, Shaheed Benazirabad, Sindh 67450, Sindh, Pakistan 

S. Khoso, Department of Civil Engineering, Quaid e Awam University of Engineering Science and Technology, Sindh, Pakistan

Department of Civil Engineering, Quaid e Awam University of Engineering Science and Technology, Sakrand Road, Nawabshah, Shaheed Benazirabad, Sindh 67450, Sindh, Pakistan 

Z. Iqbal, Faculty of Civil Engineering, Universiti Teknologi Malaysia, Malaysia

Faculty of Civil Engineering, Universiti Teknologi Malaysia, Sultan Ibrahim Chancellery Building, Jalan Iman, 81310 Skudai, Johor, Malaysia 

S. Sohu, Department of Civil Engineering, Quaid-e-Awam University College of Engineering, Science & Technology, Pakistan

Department of Civil Engineering, Quaid-e-Awam University Engineering, Science & Technology, Sakrand Road, Nawabshah, Shaheed Benazirabad, Sindh 67450, Pakistan

M. A. Keerio, Civil Engineering Department, Quaid-e-Awam University of Engineering, Science & Technology, Nawabshah, Pakistan

Department of Civil Engineering, Quaid e Awam University of Engineering Science and Technology, Sakrand Road, Nawabshah, Shaheed Benazirabad, Sindh 67450, Sindh, Pakistan 

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How to Cite
[1]
J. S. Khan, S. Khoso, Z. Iqbal, S. Sohu, and M. A. Keerio, “An Outlook of Ozone Air Pollution through Comparative Analysis of Artificial Neural Network, Regression, and Sensitivity ModelsAn Outlook of Ozone Air Pollution Through Comparative Analysis of Artificial Neural Network, Regression, and Sensitivity Models”, Eng. Technol. Appl. Sci. Res., vol. 8, no. 5, pp. 3387-3391, Oct. 2018.

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