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.
Bibliographical noteFunding Information:
This research was funded by the Deanship of Scientific Research at King Faisal University under Nasher Track (Grant No. 186357). The co-authors acknowledge the support received from King Fahd University of Petroleum and Minerals.
Funding: This research was funded by the Deanship of Scientific Research at King Faisal University under Nasher Track (Grant No. 186357).
© 2020 by the authors.
- Adaptive neuro-fuzzy inference system
- Air quality model
- Artificial neural networks
- Deep learning
- Ensemble model
- Evolutionary techniques
- Fuzzy logic model
- Soft computing model
- Support vector machine
ASJC Scopus subject areas
- Geography, Planning and Development
- Renewable Energy, Sustainability and the Environment
- Environmental Science (miscellaneous)
- Energy Engineering and Power Technology
- Management, Monitoring, Policy and Law