Adaptive Neural Network Metamodel for Short-term Prediction of Background Ozone Level

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dc.contributor.author Wahid, Herman en_US
dc.contributor.author Ha, Quang en_US
dc.contributor.author Nguyen-Duc, Hiep en_US
dc.contributor.editor Tu Bao Ho, Douglas Zuckerman, Pierre Kuonen, Akim Demaille, Ralf-Detlef Kutsche en_US
dc.date.accessioned 2012-02-02T11:10:08Z
dc.date.available 2012-02-02T11:10:08Z
dc.date.issued 2010 en_US
dc.identifier 2009008712 en_US
dc.identifier.citation Bin Wahid Herman, Ha Quang, and Nguyen-Duc Hiep 2010, 'Adaptive Neural Network Metamodel for Short-term Prediction of Background Ozone Level', , IEEE, Hanoi, , pp. 250-253. en_US
dc.identifier.issn 978-1-4244-8075-3 en_US
dc.identifier.other E1 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. en_US
dc.language English en_US
dc.publisher IEEE en_US
dc.relation.isbasedon http://dx.doi.org/10.1109/RIVF.2010.5633376 en_US
dc.title Adaptive Neural Network Metamodel for Short-term Prediction of Background Ozone Level en_US
dc.parent Proceedings of 2010 IEEE-RIVF International Conference on Computing and Communication Technologies - Research, Innovation and Vision for the Future en_US
dc.journal.volume en_US
dc.journal.number en_US
dc.publocation Hanoi en_US
dc.identifier.startpage 250 en_US
dc.identifier.endpage 253 en_US
dc.cauo.name FEIT.School of Elec, Mech and Mechatronic Systems en_US
dc.conference Verified OK en_US
dc.for 080100 en_US
dc.personcode 10914226 en_US
dc.personcode 000935 en_US
dc.personcode 0000062328 en_US
dc.percentage 100 en_US
dc.classification.name Artificial Intelligence and Image Processing en_US
dc.classification.type FOR-08 en_US
dc.edition en_US
dc.custom IEEE-RIVF International Conference on Computing and Communication Technologies, Research Innovation, and Vision for the Future en_US
dc.date.activity 20101101 en_US
dc.location.activity Hanoi, Vietnam en_US
dc.description.keywords adaptive systems , air pollution , environmental science computing , ozone , prediction theory , radial basis function networks , metamodelling , adaptive spread, background ozone trend, Sydney basin en_US
dc.staffid en_US


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