Adaptive neural network metamodel for short-term prediction of background ozone level

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

Adaptive neural network metamodel for short-term prediction of background ozone level

Wahid, H Ha, QP

Nguyen-Duc, H

Permalink

Publication Type:: Conference Proceeding
Citation:: 2010 IEEE-RIVF International Conference on Computing and Communication Technologies: Research, Innovation and Vision for the Future, RIVF 2010, 2010
Issue Date:: 2010-12-01

Closed Access

	Filename	Description	Size
	2009008712OK.pdf		245.99 kB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

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	2010 IEEE-RIVF International Conference on Computing and Communication Technologies: Research, Innovation and Vision for the Future, RIVF 2010, 2010	en_US
dc.identifier.isbn	9781424480753	en_US
dc.identifier.uri	http://hdl.handle.net/10453/16536
dc.description.abstract	Modelling is important in air quality forecasting and control. Before applying an air quality model, it is required to accurately estimate the biogenic emission. The assessment of the background ozone concentration is essential for this estimation. It has been known that the biogenic ozone level in urban areas is changing over the years, and hence information about the temporal trends in air quality data is helpful for the assessment. This paper presents a neural-network metamodel for prediction of the background ozone level in the Sydney basin. Based on measured monitoring data under non-photochemical conditions collected at a number of monitoring stations, the proposed model can reliably provide short-term predictions in the biogenic ozone trends to be used for analysis of ground-level emission impact on air quality. ©2010 IEEE.	en_US
dc.relation.ispartof	2010 IEEE-RIVF International Conference on Computing and Communication Technologies: Research, Innovation and Vision for the Future, RIVF 2010	en_US
dc.relation.isbasedon	10.1109/RIVF.2010.5633376	en_US
dc.title	Adaptive neural network metamodel for short-term prediction of background ozone level	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Hanoi, Vietnam	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
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

Modelling is important in air quality forecasting and control. Before applying an air quality model, it is required to accurately estimate the biogenic emission. The assessment of the background ozone concentration is essential for this estimation. It has been known that the biogenic ozone level in urban areas is changing over the years, and hence information about the temporal trends in air quality data is helpful for the assessment. This paper presents a neural-network metamodel for prediction of the background ozone level in the Sydney basin. Based on measured monitoring data under non-photochemical conditions collected at a number of monitoring stations, the proposed model can reliably provide short-term predictions in the biogenic ozone trends to be used for analysis of ground-level emission impact on air quality. ©2010 IEEE.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/16536