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.
Highlights
Air pollutants cause widespread detrimental effects on physical, biological, and economic systems
Artificial neural networks (ANN), support vector machines (SVM), evolutionary artificial neural networks (ANN) and SVM, and fuzzy logic and neuro-fuzzy systems are widely used in air quality modeling (AQM)
The SVM technique constructs an optimal geometric hyperplane to distinguish the available data and to map them into the higher dimensional feature space by forming a separation surface through the employment of various functions, including sigmoidal, polynomial, and radial basis functions [218,219]. While they are dealing with regression problems, the SVM is known as support vector regression
Summary
Air pollutants cause widespread detrimental effects on physical, biological, and economic systems. In recent years, other AI-based models such as functional networks, genetic programming, type-2 fuzzy logic, genetic fuzzy, genetic neuro-fuzzy, and case-based reasoning models have been successfully used to solve many complex non-linear regression problems, including oil-yield [52], particle size in a fluidized bed reactor [53], flood flows [54], and power system stability [55]. Such models have not yet been explored significantly in air quality modeling for the prediction of the air pollutant concentration levels.
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