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dc.contributor.authorNur Afiqah, Zakaria
dc.contributor.authorNorazian, Mohamed Noor
dc.date.accessioned2020-10-26T04:58:19Z
dc.date.available2020-10-26T04:58:19Z
dc.date.issued2018
dc.identifier.citationUrbanism. Arhitectură. Construcţii, vol.9(2), 2018, pages 169-166.en_US
dc.identifier.urihttp://dspace.unimap.edu.my:80/xmlui/handle/123456789/68510
dc.descriptionLink to publisher's homepage at https://uac.incd.ro/EN/index.htmen_US
dc.description.abstractThe air quality measurement data obtained from the continuous ambient air quality monitoring (CAAQM) station usually contained missing data. The missing observations of the data usually occurred due to machine failure, routine maintenance and human error. In this study, the hourly monitoring data of CO, O3, PM10, SO2, NOx, NO2, ambient temperature and humidity were used to evaluate four imputation methods (Mean Top Bottom, Linear Regression, Multiple Imputation and Nearest Neighbour). The air pollutants observations were simulated into four percentages of simulated missing data i.e. 5%, 10%, 15% and 20%. Performance measures namely the Mean Absolute Error, Root Mean Squared Error, Coefficient of Determination and Index of Agreement were used to describe the goodness of fit of the imputation methods. From the results of the performance measures, Mean Top Bottom method was selected as the most appropriate imputation method for filling in the missing values in air pollutants data.en_US
dc.language.isoenen_US
dc.publisherNR&DI URBAN-INCERCen_US
dc.subjectAir pollutionen_US
dc.subjectMissing dataen_US
dc.subjectImputation methodsen_US
dc.subjectMultiple imputationen_US
dc.titleImputation methods for filling missing data in urban air pollution data for Malaysiaen_US
dc.typeArticleen_US
dc.identifier.url2069-0509 (print)
dc.identifier.url2069-6469 (online)
dc.identifier.urlhttps://uac.incd.ro/EN/index.htm
dc.contributor.urlnurafiqahzakaria15@gmail.comen_US
dc.contributor.urlnorazian@unimap.edu.myen_US


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