A metamodel for background ozone level using radial basis function neural networks

Wahid, H; Ha, QP; Nguyen-Duc, H

A metamodel for background ozone level using radial basis function neural networks

Wahid, H Ha, QP

Nguyen-Duc, H

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Publication Type:: Conference Proceeding
Citation:: 11th International Conference on Control, Automation, Robotics and Vision, ICARCV 2010, 2010, pp. 958 - 963
Issue Date:: 2010-12-01

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Field	Value	Language
dc.contributor.author	Wahid, H	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.date.issued	2010-12-01	en_US
dc.identifier.citation	11th International Conference on Control, Automation, Robotics and Vision, ICARCV 2010, 2010, pp. 958 - 963	en_US
dc.identifier.isbn	9781424478132	en_US
dc.identifier.uri	http://hdl.handle.net/10453/16481
dc.description.abstract	In air quality modelling, determination of the background ozone level is essential as it highly affects the accuracy of the photochemical air quality model. It is known that the background ozone level, especially in urban areas, has been changing over the years. Unfortunately, the reasons of that alteration were not clear and the background ozone itself was not easily derived in practice. In this paper, a new background ozone model will be developed by using the ozone ambient quality data and the meteorological data at the several stations in the Sydney basin. To accomplish the modelling process, an adaptively-tuned radial basis function neural network metamodel is proposed and utilised in the simulation. Different input parameters are considered to evaluate their influence on the constructed background ozone model. The proposed model, subject to some statistical criteria, demonstrates its capability of estimating the background ozone level with a reasonably good accuracy. ©2010 IEEE.	en_US
dc.relation.ispartof	11th International Conference on Control, Automation, Robotics and Vision, ICARCV 2010	en_US
dc.relation.isbasedon	10.1109/ICARCV.2010.5707302	en_US
dc.title	A metamodel for background ozone level using radial basis function neural networks	en_US
dc.type	Conference Proceeding
utslib.for	090602 Control Systems, Robotics and Automation	en_US
dc.location.activity	Singapore	en_US
dc.location.activity	Singapore, SINGAPORE
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.publication-status	Published	en_US

Abstract:

In air quality modelling, determination of the background ozone level is essential as it highly affects the accuracy of the photochemical air quality model. It is known that the background ozone level, especially in urban areas, has been changing over the years. Unfortunately, the reasons of that alteration were not clear and the background ozone itself was not easily derived in practice. In this paper, a new background ozone model will be developed by using the ozone ambient quality data and the meteorological data at the several stations in the Sydney basin. To accomplish the modelling process, an adaptively-tuned radial basis function neural network metamodel is proposed and utilised in the simulation. Different input parameters are considered to evaluate their influence on the constructed background ozone model. The proposed model, subject to some statistical criteria, demonstrates its capability of estimating the background ozone level with a reasonably good accuracy. ©2010 IEEE.

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