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Soft Computing Applications in Air Quality Modeling: Past, Present, and Future

Author

Listed:
  • Muhammad Muhitur Rahman

    (Department of Civil and Environmental Engineering, College of Engineering, King Faisal University, Al-Ahsa 31982, Saudi Arabia)

  • Md Shafiullah

    (Center of Research Excellence in Renewable Energy, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia)

  • Syed Masiur Rahman

    (Center for Environment & Water, Research Institute, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia)

  • Abu Nasser Khondaker

    (Center for Environment & Water, Research Institute, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia)

  • Abduljamiu Amao

    (Center for Integrative Petroleum Research, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia)

  • Md. Hasan Zahir

    (Center of Research Excellence in Renewable Energy, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia)

Abstract

Air quality models simulate the atmospheric environment systems and provide increased domain knowledge and reliable forecasting. They provide early warnings to the population and reduce the number of measuring stations. Due to the complexity and non-linear behavior associated with air quality data, soft computing models became popular in air quality modeling (AQM). This study critically investigates, analyses, and summarizes the existing soft computing modeling approaches. Among the many soft computing techniques in AQM, this article reviews and discusses artificial neural network (ANN), support vector machine (SVM), evolutionary ANN and SVM, the fuzzy logic model, neuro-fuzzy systems, the deep learning model, ensemble, and other hybrid models. Besides, it sheds light on employed input variables, data processing approaches, and targeted objective functions during modeling. It was observed that many advanced, reliable, and self-organized soft computing models like functional network, genetic programming, type-2 fuzzy logic, genetic fuzzy, genetic neuro-fuzzy, and case-based reasoning are rarely explored in AQM. Therefore, the partially explored and unexplored soft computing techniques can be appropriate choices for research in the field of air quality modeling. The discussion in this paper will help to determine the suitability and appropriateness of a particular model for a specific modeling context.

Suggested Citation

  • Muhammad Muhitur Rahman & Md Shafiullah & Syed Masiur Rahman & Abu Nasser Khondaker & Abduljamiu Amao & Md. Hasan Zahir, 2020. "Soft Computing Applications in Air Quality Modeling: Past, Present, and Future," Sustainability, MDPI, vol. 12(10), pages 1-33, May.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:10:p:4045-:d:358319
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    1. Shankar Subramaniam & Naveenkumar Raju & Abbas Ganesan & Nithyaprakash Rajavel & Maheswari Chenniappan & Chander Prakash & Alokesh Pramanik & Animesh Kumar Basak & Saurav Dixit, 2022. "Artificial Intelligence Technologies for Forecasting Air Pollution and Human Health: A Narrative Review," Sustainability, MDPI, vol. 14(16), pages 1-36, August.
    2. Sasikumar Gurumoorthy & Aruna Kumari Kokku & Przemysław Falkowski-Gilski & Parameshachari Bidare Divakarachari, 2023. "Effective Air Quality Prediction Using Reinforced Swarm Optimization and Bi-Directional Gated Recurrent Unit," Sustainability, MDPI, vol. 15(14), pages 1-19, July.
    3. Paulo S. G. de Mattos Neto & Manoel H. N. Marinho & Hugo Siqueira & Yara de Souza Tadano & Vivian Machado & Thiago Antonini Alves & João Fausto L. de Oliveira & Francisco Madeiro, 2020. "A Methodology to Increase the Accuracy of Particulate Matter Predictors Based on Time Decomposition," Sustainability, MDPI, vol. 12(18), pages 1-33, September.
    4. Justyna Kujawska & Monika Kulisz & Piotr Oleszczuk & Wojciech Cel, 2022. "Machine Learning Methods to Forecast the Concentration of PM10 in Lublin, Poland," Energies, MDPI, vol. 15(17), pages 1-23, September.

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