Univariate time series based radial basis function neural network modeling of air quality inside a public transportation bus using available software. Issue 2 (8th March 2016)

• Record Type: Journal Article
• Authors: Kadiyala, Akhil; Kumar, Ashok
• Is Part Of: Environmental progress & sustainable energy. Volume 35:Issue 2(2016:Jul.)
• Page Start: 320
• Page End: 324
• Publication Date: 2016-03-08
• Subjects: radial basis function -- neural networks -- time series -- ARMA/ARIMA -- indoor air quality -- in‐bus air contaminants -- biodiesel -- public transportation buses -- operational performance measures; Environmental engineering -- Periodicals; Sustainable engineering -- Periodicals; Environmental chemistry -- Periodicals; 333.7
• DOI: 10.1002/ep.12349
• Languages: English
• ISSNs: 1944-7442

Abstract

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