Predicting carbon monoxide emissions with multivariate adaptive regression splines (MARS) and artificial neural networks (ANNs)

Oduro, SD; Metia, S; Duc, H; Ha, QP

Predicting carbon monoxide emissions with multivariate adaptive regression splines (MARS) and artificial neural networks (ANNs)

Oduro, SD Metia, S Duc, H Ha, QP

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Publication Type:: Conference Proceeding
Citation:: 32nd International Symposium on Automation and Robotics in Construction and Mining: Connected to the Future, Proceedings, 2015
Issue Date:: 2015-01-01

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Field	Value	Language
dc.contributor.author	Oduro, SD	en_US
dc.contributor.author	Metia, S	en_US
dc.contributor.author	Duc, H	en_US
dc.contributor.author	Ha, QP	en_US
dc.date.issued	2015-01-01	en_US
dc.identifier.citation	32nd International Symposium on Automation and Robotics in Construction and Mining: Connected to the Future, Proceedings, 2015	en_US
dc.identifier.isbn	9789517585972	en_US
dc.identifier.uri	http://hdl.handle.net/10453/60805
dc.description.abstract	Emissions from motor vehicles need to be predicted fairly accurately to ensure an appropriate air quality management plan. This research work explores the use of a nonparametric regression algorithm known as the multivariate adaptive regression splines (MARS) in comparison with the artificial neural networks (ANN) for the purpose of best approximation of the relationship between the input and output from datasets recorded from on-board measurement and dynamometer testings. The performance of the models was evaluated by comparing the MARS and ANN predictions to the measured data using several performance indices. The results are evaluated in terms of accuracy, flexibility and computational efficiency. While MARS are more computationally efficient to reach the final model ANN are slightly more accurate. The proposed techniques may be used to assist in a decision-making policy regarding urban air pollution.	en_US
dc.relation.ispartof	32nd International Symposium on Automation and Robotics in Construction and Mining: Connected to the Future, Proceedings	en_US
dc.title	Predicting carbon monoxide emissions with multivariate adaptive regression splines (MARS) and artificial neural networks (ANNs)	en_US
dc.type	Conference Proceeding
utslib.for	090506 Structural Engineering	en_US
utslib.for	1099 Other Technology	en_US
utslib.for	090602 Control Systems, Robotics and Automation	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CBI - Centre for Built Infrastructure
pubs.organisational-group	/University of Technology Sydney/Strength - GEVI - Green Energy and Vehicle Innovations
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

Emissions from motor vehicles need to be predicted fairly accurately to ensure an appropriate air quality management plan. This research work explores the use of a nonparametric regression algorithm known as the multivariate adaptive regression splines (MARS) in comparison with the artificial neural networks (ANN) for the purpose of best approximation of the relationship between the input and output from datasets recorded from on-board measurement and dynamometer testings. The performance of the models was evaluated by comparing the MARS and ANN predictions to the measured data using several performance indices. The results are evaluated in terms of accuracy, flexibility and computational efficiency. While MARS are more computationally efficient to reach the final model ANN are slightly more accurate. The proposed techniques may be used to assist in a decision-making policy regarding urban air pollution.

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http://hdl.handle.net/10453/60805