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Pollutant Profile Estimation Using Unscented Kalman Filter

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dc.contributor.author Metia S.
dc.contributor.author Oduro S.D.
dc.contributor.author Sinha A.P.
dc.date.accessioned 2022-10-31T15:05:25Z
dc.date.available 2022-10-31T15:05:25Z
dc.date.issued 2020
dc.identifier.issn 18761100
dc.identifier.other 10.1007/978-981-32-9346-5_2
dc.identifier.uri http://41.74.91.244:8080/handle/123456789/416
dc.description Metia, S., Faculty of Engineering and IT, University of Technology Sydney, Sydney, Australia; Oduro, S.D., Department of Mechanical and Automotive Technology Education College of Technology Education Kumasi, University of Education Winneba, Kumasi, Ghana; Sinha, A.P., Department of Electronics and Communication Engineering, BIT Sindri, Dhanbad, India en_US
dc.description.abstract In this paper, we develop an estimation model for carbon monoxide (CO) air pollution concentrations. CO is an important pollutant which is used to calculate an air quality index (AQI). AQI becomes less reliable as the proportion of data missing due to equipment failure and periods of calibration increases. This paper presents the Unscented Kalman filter (UKF) to predict missing data of atmospheric carbon monoxide concentrations using the time series data of monitoring stations. � 2020, Springer Nature Singapore Pte Ltd. en_US
dc.publisher Springer en_US
dc.subject Air quality index (AQI) en_US
dc.subject Carbon monoxide (CO) en_US
dc.subject Unscented Kalman filter (UKF) en_US
dc.title Pollutant Profile Estimation Using Unscented Kalman Filter en_US
dc.type Conference Paper en_US


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