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Multivariate 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.12387
Subject(s) - multivariate statistics , artificial neural network , air quality index , software , indoor air quality , multivariate analysis , process (computing) , radial basis function , computer science , time series , function (biology) , engineering , environmental science , artificial intelligence , machine learning , environmental engineering , meteorology , geography , evolutionary biology , biology , programming language , operating system
The development of valid air quality models (adressing the complex interrelationships between air contaminants and influential variables) is an integral component to developing good indoor air quality (IAQ) management strategies. With an increase in the capabilities of computational resources to process large datasets utilizing the hybrid mathematical calculations, environmentalists are now better equipped to develop and use hybrid IAQ models. This software review paper presents the development and evaluation of one such hybrid IAQ model, referred to as the multivariate time series based radial basis function neural network models (multivariate time series + radial basis function neural networks) 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: 931–935, 2016