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Multivariate time series based back propagation neural network modeling of air quality inside a public transportation bus using available software
Author(s) -
Kadiyala Akhil,
Kumar Ashok
Publication year - 2015
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.12199
Subject(s) - multivariate statistics , artificial neural network , software , indoor air quality , air quality index , computer science , transport engineering , engineering , machine learning , environmental engineering , meteorology , geography , programming language
Time series and artificial neural networks (ANNs) are two distinct methodologies that present environmental researchers and managers with the resources to develop reliable and accurate indoor air quality (IAQ) models. The development of valid IAQ models is a fundamental component in the development of risk assessment and mitigation plans that ensure an occupant's exposure to indoor air contaminants are within the permissible IAQ guidelines. This software review paper provides a detailed step‐by‐step description of the methodology on how one may integrate the use of the multivariate time series and the back propagation neural network (widely used ANN) methods simultaneously in the development of valid in‐bus carbon dioxide and carbon monoxide models using available software. © 2015 American Institute of Chemical Engineers Environ Prog, 34: 1259–1266, 2015