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

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@Article{rahman:2020:Sustainability,

• author = "Muhammad Muhitur Rahman and Md Shafiullah and Syed Masiur Rahman and Abu Nasser Khondaker and Abduljamiu Amao and Md. Hasan Zahir",
• title = "Soft Computing Applications in Air Quality Modeling: Past, Present, and Future",
• journal = "Sustainability",
• year = "2020",
• volume = "12",
• number = "10",
• keywords = "genetic algorithms, genetic programming",
• ISSN = "2071-1050",
• URL = "https://www.mdpi.com/2071-1050/12/10/4045",
• DOI = "doi:10.3390/su12104045",
• 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 behaviour associated with air quality data, soft computing models became popular in air quality modelling (AQM). This study critically investigates, analyses, and summarizes the existing soft computing modelling 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 modelling. 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 modelling. The discussion in this paper will help to determine the suitability and appropriateness of a particular model for a specific modelling context.",
• notes = "also known as \cite{su12104045}",

}

Genetic Programming entries for Muhammad Muhitur Rahman Md Shafiullah Syed Masiur Rahman Abu Nasser Khondaker Abduljamiu Olalekan Amao Md Hasan Zahir

Citations