Estimation of Background Ozone Temporal Profiles using Neural Networks

Wahid, HB; Ha, QP; Nguyen-Duc, H; Azzi, M

Estimation of Background Ozone Temporal Profiles using Neural Networks

Wahid, HB Ha, QP

Nguyen-Duc, H Azzi, M

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Publisher:: IEEE Press
Publication Type:: Conference Proceeding
Citation:: Proceedings 2011 IEEE International Conference on Intelligent Computing and Intelligent Systems (ICIS 2011), 2011, pp. 292 - 297
Issue Date:: 2011-01

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Field	Value	Language
dc.contributor.author	Wahid, HB	en_US
dc.contributor.author	Ha, QP https://orcid.org/0000-0003-0978-1758	en_US
dc.contributor.author	Nguyen-Duc, H	en_US
dc.contributor.author	Azzi, M	en_US
dc.contributor.editor	Chen, W	en_US
dc.contributor.editor	Li, S	en_US
dc.date	2011-11-18	en_US
dc.date.issued	2011-01	en_US
dc.identifier.citation	Proceedings 2011 IEEE International Conference on Intelligent Computing and Intelligent Systems (ICIS 2011), 2011, pp. 292 - 297	en_US
dc.identifier.isbn	978-1-61284-142-7	en_US
dc.identifier.uri	http://hdl.handle.net/10453/31226
dc.description.abstract	It is recognised that effective determination of the background ozone level (BOL) is important to provide the reference level in which human health risk assessment can be undertaken. The concept of BOL may be easily understood but in practice it is hard to distinguish between natural and anthropogenic effects. Apart from existing approaches to the BOL determination, a new quantisation method will be presented in this work, by evaluating the ozone versus nitric oxide (O3-NO) relationship to estimate the BOL mean value. To this end, a computational intelligence approach using the radial basis function neural network (RBFNN) is proposed for temporal estimation of the background ozone level. An improved method called forward selection with weighted least squares (FSWLS) will be introduced to select the network centres. This can beneficially result in a minimal number of hidden neurons used, especially when dealing with noisy data. The developed neural network will be utilised to map the non-linear relationship between ozone precursors and other factors in ozone generation as the inputs, with the background ozone level as the output. The resulting metamodel, subject to some statistical criteria, demonstrates its capability of estimating the background ozone temporal profiles with a reasonably good accuracy.	en_US
dc.publisher	IEEE Press	en_US
dc.relation.ispartof	Proceedings 2011 IEEE International Conference on Intelligent Computing and Intelligent Systems (ICIS 2011)	en_US
dc.relation.ispartof	IEEE International Conference on Intelligent Computing and Intelligent Systems	en_US
dc.rights	© 2011 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.title	Estimation of Background Ozone Temporal Profiles using Neural Networks	en_US
dc.type	Conference Proceeding
utslib.location	Beijing China	en_US
utslib.location.activity	Guangzhou	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
dc.location.activity	Guangzhou
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
utslib.copyright.status	closed_access
pubs.consider-herdc	true	en_US
pubs.finish-date	2011-11-20	en_US
pubs.place-of-publication	Beijing China	en_US
pubs.start-date	2011-11-18	en_US

Abstract:

It is recognised that effective determination of the background ozone level (BOL) is important to provide the reference level in which human health risk assessment can be undertaken. The concept of BOL may be easily understood but in practice it is hard to distinguish between natural and anthropogenic effects. Apart from existing approaches to the BOL determination, a new quantisation method will be presented in this work, by evaluating the ozone versus nitric oxide (O3-NO) relationship to estimate the BOL mean value. To this end, a computational intelligence approach using the radial basis function neural network (RBFNN) is proposed for temporal estimation of the background ozone level. An improved method called forward selection with weighted least squares (FSWLS) will be introduced to select the network centres. This can beneficially result in a minimal number of hidden neurons used, especially when dealing with noisy data. The developed neural network will be utilised to map the non-linear relationship between ozone precursors and other factors in ozone generation as the inputs, with the background ozone level as the output. The resulting metamodel, subject to some statistical criteria, demonstrates its capability of estimating the background ozone temporal profiles with a reasonably good accuracy.

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http://hdl.handle.net/10453/31226