Environmental time series analysis and estimation with extended Kalman filtering

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

Environmental time series analysis and estimation with extended Kalman filtering

Metia, S

Oduro, SD Ha, QP

Duc, H Azzi, M

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Publication Type:: Conference Proceeding
Citation:: Proceedings - 1st International Conference on Artificial Intelligence, Modelling and Simulation, AIMS 2013, 2014, pp. 235 - 240
Issue Date:: 2014-11-14

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Field	Value	Language
dc.contributor.author	Metia, S https://orcid.org/0000-0002-5676-1146	en_US
dc.contributor.author	Oduro, SD	en_US
dc.contributor.author	Ha, QP https://orcid.org/0000-0003-0978-1758	en_US
dc.contributor.author	Duc, H	en_US
dc.contributor.author	Azzi, M	en_US
dc.date.issued	2014-11-14	en_US
dc.identifier.citation	Proceedings - 1st International Conference on Artificial Intelligence, Modelling and Simulation, AIMS 2013, 2014, pp. 235 - 240	en_US
dc.identifier.isbn	9781479932511	en_US
dc.identifier.uri	http://hdl.handle.net/10453/26596
dc.description.abstract	© 2013 IEEE. This paper addresses the problem of air pollutant profile estimation by using measurements collected from different weather stations. An algorithm is developed, based on an Extended Kalman Filter to handle missing temporal data and using the statistical Kriging method to interpolate spatial data. Combination of extended Kalman filtering with Matérn covariance function has proven to be useful in exploiting meteorological information to build reliable air quality models. We have applied the developed algorithm to estimate air pollutant profiles in the Sydney basin, which is subject to a variety of pollutant sources, including fossil-fueled electric power generation plants, high motor vehicle usage, aviation and shipping traffic. The results have shown that the proposed approach can improve accuracy of the estimation profiles.	en_US
dc.relation.ispartof	Proceedings - 1st International Conference on Artificial Intelligence, Modelling and Simulation, AIMS 2013	en_US
dc.relation.isbasedon	10.1109/AIMS.2013.44	en_US
dc.title	Environmental time series analysis and estimation with extended Kalman filtering	en_US
dc.type	Conference Proceeding
utslib.for	080109 Pattern Recognition and Data Mining	en_US
dc.location.activity	Kota Kinabalu, Sabah, Malaysia	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/Students
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

© 2013 IEEE. This paper addresses the problem of air pollutant profile estimation by using measurements collected from different weather stations. An algorithm is developed, based on an Extended Kalman Filter to handle missing temporal data and using the statistical Kriging method to interpolate spatial data. Combination of extended Kalman filtering with Matérn covariance function has proven to be useful in exploiting meteorological information to build reliable air quality models. We have applied the developed algorithm to estimate air pollutant profiles in the Sydney basin, which is subject to a variety of pollutant sources, including fossil-fueled electric power generation plants, high motor vehicle usage, aviation and shipping traffic. The results have shown that the proposed approach can improve accuracy of the estimation profiles.

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http://hdl.handle.net/10453/26596