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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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    References listed on IDEAS

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    1. Ali Azadeh & Mohammad Sheikhalishahi & Morteza Saberi & M.H. Mostaghimi, 2013. "An intelligent multivariate approach for optimum forecasting of daily ozone concentration in large metropolitans with incomplete inputs," International Journal of Productivity and Quality Management, Inderscience Enterprises Ltd, vol. 12(2), pages 209-225.
    2. Paulo S G de Mattos Neto & George D C Cavalcanti & Francisco Madeiro & Tiago A E Ferreira, 2015. "An Approach to Improve the Performance of PM Forecasters," PLOS ONE, Public Library of Science, vol. 10(9), pages 1-23, September.
    3. Gong Bing & Joaquín Ordieres-Meré & Claudia Barreto Cabrera, 2015. "Prediction models for ozone in metropolitan area of Mexico City based on artificial intelligence techniques," International Journal of Information and Decision Sciences, Inderscience Enterprises Ltd, vol. 7(2), pages 115-139.
    4. Bing-Chun Liu & Arihant Binaykia & Pei-Chann Chang & Manoj Kumar Tiwari & Cheng-Chin Tsao, 2017. "Urban air quality forecasting based on multi-dimensional collaborative Support Vector Regression (SVR): A case study of Beijing-Tianjin-Shijiazhuang," PLOS ONE, Public Library of Science, vol. 12(7), pages 1-17, July.
    5. Khaled J. Assi & Md Shafiullah & Kh Md Nahiduzzaman & Umer Mansoor, 2019. "Travel-To-School Mode Choice Modelling Employing Artificial Intelligence Techniques: A Comparative Study," Sustainability, MDPI, vol. 11(16), pages 1-12, August.
    6. Sharifian, Amir & Ghadi, M. Jabbari & Ghavidel, Sahand & Li, Li & Zhang, Jiangfeng, 2018. "A new method based on Type-2 fuzzy neural network for accurate wind power forecasting under uncertain data," Renewable Energy, Elsevier, vol. 120(C), pages 220-230.
    7. Shafaei Bajestani, Narges & Vahidian Kamyad, Ali & Nasli Esfahani, Ensieh & Zare, Assef, 2018. "Prediction of retinopathy in diabetic patients using type-2 fuzzy regression model," European Journal of Operational Research, Elsevier, vol. 264(3), pages 859-869.
    8. Md Shafiullah & M. A. Abido & Taher Abdel-Fattah, 2018. "Distribution Grids Fault Location employing ST based Optimized Machine Learning Approach," Energies, MDPI, vol. 11(9), pages 1-23, September.
    9. Chen, Shuixia & Wang, Jian-qiang & Zhang, Hong-yu, 2019. "A hybrid PSO-SVM model based on clustering algorithm for short-term atmospheric pollutant concentration forecasting," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 41-54.
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    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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