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Univariate time series based radial basis function neural network modeling of air quality inside a public transportation bus using available software
Author(s) -
Kadiyala Akhil,
Kumar Ashok
Publication year - 2016
Publication title -
environmental progress and sustainable energy
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.495
H-Index - 66
eISSN - 1944-7450
pISSN - 1944-7442
DOI - 10.1002/ep.12349
Subject(s) - univariate , artificial neural network , air quality index , software , computer science , indoor air quality , scope (computer science) , time series , operations research , engineering , artificial intelligence , machine learning , environmental engineering , meteorology , geography , multivariate statistics , programming language
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