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Forecasting particulate matter concentration using linear and non-linear approaches for air quality decision support

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dc.citedby42
dc.contributor.authorAbdullah S.en_US
dc.contributor.authorIsmail M.en_US
dc.contributor.authorAhmed A.N.en_US
dc.contributor.authorAbdullah A.M.en_US
dc.contributor.authorid56509029800en_US
dc.contributor.authorid57210403363en_US
dc.contributor.authorid57214837520en_US
dc.contributor.authorid57193067284en_US
dc.date.accessioned2023-05-29T07:22:48Z
dc.date.available2023-05-29T07:22:48Z
dc.date.issued2019
dc.descriptionAir quality; Decision support systems; Fog; Forecasting; Functions; Linear regression; Nonlinear systems; Particles (particulate matter); Radial basis function networks; Forecasting algorithm; Forecasting performance; Malaysia; Multi layer perceptron; Multiple linear regressions; Particulate Matter; Radial basis functions; Urban and rural areas; Urban growth; air quality; algorithm; decision support system; early warning system; forecasting method; multiple regression; particulate matter; performance assessment; precision; Malaysia; West Malaysiaen_US
dc.description.abstractAir quality status on the East Coast of Peninsular Malaysia is dominated by Particulate Matter (PM10) throughout the years. Studies have affirmed that PM10 influence human health and the environment. Therefore, precise forecasting algorithms are urgently needed to determine the PM10 status for mitigation plan and early warning purposes. This study investigates the forecasting performance of a linear (Multiple Linear Regression) and two non-linear models (Multi-Layer Perceptron and Radial Basis Function) utilizing meteorological and gaseous pollutants variables as input parameters from the year 2000-2014 at four sites with different surrounding activities of urban, sub-urban and rural areas. Non-linear model (Radial Basis Function) outperforms the linear model with the error reduced by 78.9% (urban), 32.1% (sub-urban) and 39.8% (rural). Association between PM10 and its contributing factors are complex and non-linear in nature, best captured by an Artificial Neural Network, which generates more accurate PM10 compared to the linear model. The results are robust enough for precise next day forecasting of PM10 concentration on the East Coast of Peninsular Malaysia. � 2019 by the authors.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo667
dc.identifier.doi10.3390/atmos10110667
dc.identifier.issue11
dc.identifier.scopus2-s2.0-85075648096
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85075648096&doi=10.3390%2fatmos10110667&partnerID=40&md5=55cf764b235e3675d27d502fa28725c3
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/24328
dc.identifier.volume10
dc.publisherMDPI AGen_US
dc.relation.ispartofAll Open Access, Gold
dc.sourceScopus
dc.sourcetitleAtmosphere
dc.titleForecasting particulate matter concentration using linear and non-linear approaches for air quality decision supporten_US
dc.typeArticleen_US
dspace.entity.typePublication
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