Publication:
Forecasting particulate matter (PM10) concentration: A radial basis function neural network approach

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dc.citedby4
dc.contributor.authorAbdullah S.en_US
dc.contributor.authorIsmail M.en_US
dc.contributor.authorGhazali N.A.en_US
dc.contributor.authorAhmed A.N.en_US
dc.contributor.authorid56509029800en_US
dc.contributor.authorid57210403363en_US
dc.contributor.authorid26430938300en_US
dc.contributor.authorid57214837520en_US
dc.date.accessioned2023-05-29T06:50:48Z
dc.date.available2023-05-29T06:50:48Z
dc.date.issued2018
dc.description.abstractParticulate matter is a prevalent pollutant that affects human health and the environment. Local authorities need a precise PM10 concentration forecasting model as the information can be used to take precautionary measures and significant actions can be taken to improve air quality status. This study trained and tested the nonlinear model, namely Radial Basis Function (RBF) in an industrial area of Pasir Gudang, Johor. Daily observations of PM10 concentration, meteorological factors (wind speed, ambient temperature, and relative humidity) and gaseous pollutants (SO2, NO2, and CO) from the year 2010-2014 were used in this study. Results showed that RBF model was able to explain 65.2% (R2 = 0.652) and 84.9% (R2 = 0.849) variance in the data during training and testing, respectively. Thus, it is proven that nonlinear model has high ability in virtually representing the complexity and nonlinearity of PM10 in the atmosphere without any prior assumptions. � 2018 Author(s).en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo20043
dc.identifier.doi10.1063/1.5062669
dc.identifier.scopus2-s2.0-85055571664
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85055571664&doi=10.1063%2f1.5062669&partnerID=40&md5=4e204f5ec338c952a81fe27a38dad1a9
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/23657
dc.identifier.volume2020
dc.publisherAmerican Institute of Physics Inc.en_US
dc.relation.ispartofAll Open Access, Bronze
dc.sourceScopus
dc.sourcetitleAIP Conference Proceedings
dc.titleForecasting particulate matter (PM10) concentration: A radial basis function neural network approachen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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