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Neural network fitting using levenberg-marquardt training algorithm for PM10 concentration forecasting in Kuala Terengganu

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dc.citedby2
dc.contributor.authorAbdullah S.en_US
dc.contributor.authorIsmail M.en_US
dc.contributor.authorFong S.Y.en_US
dc.contributor.authorAhmed A.N.en_US
dc.contributor.authorid56509029800en_US
dc.contributor.authorid57210403363en_US
dc.contributor.authorid57189591438en_US
dc.contributor.authorid57214837520en_US
dc.date.accessioned2023-05-29T06:13:13Z
dc.date.available2023-05-29T06:13:13Z
dc.date.issued2016
dc.description.abstractThe forecasting of Particulate Matter (PM10) is crucial as the information can be used by local authority in informing community regarding the level air quality at specific location. The non-linearity of PM10 in atmosphere after it was subjected by several meteorological parameters should be treated with powerful statistical models which can provide high accuracy in forecasting the PM10 concentration for instance Neural Network (NN) model. Thus, the aim of this study is establishment of NN model using Levenberg-Marquardt training algorithm with meteorological parameters as predictors. Daily observations of PM10, wind speed, relative humidity, ambient temperature, rainfall, and atmospheric pressure in Kuala Terengganu, Malaysia from January 2009 to December 2014 were selected for predicting PM10 concentration level. Principal Component Analysis (PCA) was applied prior the establishment of NN model with the aim of reducing multi-collinearity among predictors. The three principal components (PC-1, PC-2, PC-3) as the result of PCA was used as the input for the NN model. The NN model with 14 hidden neurons was found as the best model having MSE of 0.00164 and R values of 0.80435 (Training stage), 0.85735 (Validation stage), and 0.8135 (Testing stage). Overall the model performance was achieved as high as 81.1% for PM10 forecasting in Kuala Terengganu.en_US
dc.description.natureFinalen_US
dc.identifier.epage31
dc.identifier.issue12
dc.identifier.scopus2-s2.0-85011390848
dc.identifier.spage27
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85011390848&partnerID=40&md5=977d1feee34025155292554bd11ad0be
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/22901
dc.identifier.volume8
dc.publisherUniversiti Teknikal Malaysia Melakaen_US
dc.sourceScopus
dc.sourcetitleJournal of Telecommunication, Electronic and Computer Engineering
dc.titleNeural network fitting using levenberg-marquardt training algorithm for PM10 concentration forecasting in Kuala Terengganuen_US
dc.typeArticleen_US
dspace.entity.typePublication
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