Air pollution prediction using Matérn function based extended fractional Kalman filtering

Metia, S; Oduro, SD; Ha, QP; Due, H

Air pollution prediction using Matérn function based extended fractional Kalman filtering

Metia, S

Oduro, SD Ha, QP

Due, H

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Publication Type:: Conference Proceeding
Citation:: 2014 13th International Conference on Control Automation Robotics and Vision, ICARCV 2014, 2014, pp. 758 - 763
Issue Date:: 2014-01-01

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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	Due, H	en_US
dc.date.issued	2014-01-01	en_US
dc.identifier.citation	2014 13th International Conference on Control Automation Robotics and Vision, ICARCV 2014, 2014, pp. 758 - 763	en_US
dc.identifier.isbn	9781479951994	en_US
dc.identifier.uri	http://hdl.handle.net/10453/31066
dc.description.abstract	© 2014 IEEE. It is essential to maintain air quality standards and inform people when air pollutant concentrations exceed permissible limits. For example, ground-level ozone, a harmful gas formed by NOx and VOCs emitted from various sources, can be estimated through integration of observation data obtained from measurement sites and effective air-quality models. This paper addresses the problem of predicting air pollution emissions over urban and suburban areas using The Air Pollution Model with Chemical Transport Model (TAPM-CTM) coupled with the Extended Fractional Kaiman Filter (EFKF) based on a Matern covariance function. Here, the ozone concentration is predicted in the airshed of Sydney and surrounding areas, where the length scale parameter I is calculated using station coordinates. For improvement of the air quality prediction, the fractional order of the EFKF is tuned by using a Genetic Algorithm (GA). The proposed methodology is validated at monitoring stations and applied to obtain a spatial distribution of ozone over the region.	en_US
dc.relation.ispartof	2014 13th International Conference on Control Automation Robotics and Vision, ICARCV 2014	en_US
dc.relation.isbasedon	10.1109/ICARCV.2014.7064399	en_US
dc.title	Air pollution prediction using Matérn function based extended fractional Kalman filtering	en_US
dc.type	Conference Proceeding
utslib.for	0906 Electrical and Electronic Engineering	en_US
dc.location.activity	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
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

© 2014 IEEE. It is essential to maintain air quality standards and inform people when air pollutant concentrations exceed permissible limits. For example, ground-level ozone, a harmful gas formed by NOx and VOCs emitted from various sources, can be estimated through integration of observation data obtained from measurement sites and effective air-quality models. This paper addresses the problem of predicting air pollution emissions over urban and suburban areas using The Air Pollution Model with Chemical Transport Model (TAPM-CTM) coupled with the Extended Fractional Kaiman Filter (EFKF) based on a Matern covariance function. Here, the ozone concentration is predicted in the airshed of Sydney and surrounding areas, where the length scale parameter I is calculated using station coordinates. For improvement of the air quality prediction, the fractional order of the EFKF is tuned by using a Genetic Algorithm (GA). The proposed methodology is validated at monitoring stations and applied to obtain a spatial distribution of ozone over the region.

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http://hdl.handle.net/10453/31066