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- W4252626082 abstract "Modern indoor air quality (IAQ) management plans integrate the use of valid air quality models that accurately predict the dynamics of air quality variations within a considered environment. With rapid advancements in the field of computer sciences that helped improve the capabilities of computational resources and the availability of a wide-ranging spectrum of methodologies that address different aspects of the nonlinearity in a multi-dimensional information domain, there is ample scope for environmental professionals to develop and use hybrid IAQ models. This software review paper presents one such methodology that combines the use of the univariate time series and the radial basis function neural network methods in the development and evaluation of univariate time series based radial basis function neural network hybrid IAQ models for the monitored contaminants of carbon dioxide and carbon monoxide inside a public transportation bus using available software. © 2016 American Institute of Chemical Engineers Environ Prog, 35: 320–324, 2016" @default.
- W4252626082 created "2022-05-12" @default.
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- W4252626082 date "2016-03-04" @default.
- W4252626082 modified "2023-10-16" @default.
- W4252626082 title "Univariate time series based radial basis function neural network modeling of air quality inside a public transportation bus using available software" @default.
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- W4252626082 doi "https://doi.org/10.1002/ep.12349" @default.
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