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Developing machine learning algorithms for meteorological temperature and humidity forecasting at Terengganu state in Malaysia

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dc.citedby20
dc.contributor.authorHanoon M.S.en_US
dc.contributor.authorAhmed A.N.en_US
dc.contributor.authorZaini N.en_US
dc.contributor.authorRazzaq A.en_US
dc.contributor.authorKumar P.en_US
dc.contributor.authorSherif M.en_US
dc.contributor.authorSefelnasr A.en_US
dc.contributor.authorEl-Shafie A.en_US
dc.contributor.authorid57266877500en_US
dc.contributor.authorid57214837520en_US
dc.contributor.authorid56905328500en_US
dc.contributor.authorid57219410567en_US
dc.contributor.authorid57206939156en_US
dc.contributor.authorid7005414714en_US
dc.contributor.authorid6505592467en_US
dc.contributor.authorid16068189400en_US
dc.date.accessioned2023-05-29T09:05:23Z
dc.date.available2023-05-29T09:05:23Z
dc.date.issued2021
dc.descriptionair temperature; article; forecasting; linear regression analysis; multilayer perceptron; prediction; radial basis function; random forest; relative humidity; Terengganuen_US
dc.description.abstractAccurately predicting meteorological parameters such as air temperature and humidity plays a crucial role in air quality management. This study proposes different machine learning algorithms: Gradient Boosting Tree (G.B.T.), Random forest (R.F.), Linear regression (LR) and different artificial neural network (ANN) architectures (multi-layered perceptron, radial basis function) for prediction of such as air temperature (T) and relative humidity (Rh). Daily data over 24�years for Kula Terengganu station were obtained from the Malaysia Meteorological Department. Results showed that MLP-NN performs well among the others in predicting daily T and Rh with R of 0.7132 and 0.633, respectively. However, in monthly prediction T also MLP-NN model provided closer standards deviation to actual value and can be used to predict monthly T with R 0.8462. Whereas in prediction monthly Rh, the RBF-NN model's efficiency was higher than other models with R of 0.7113. To validate the performance of the trained both artificial neural network (ANN) architectures MLP-NN and RBF-NN, both were applied to an unseen data set from observation data in the region. The results indicated that on either architecture of ANN, there is good potential to predict daily and monthly T and Rh values with an acceptable range of accuracy. � 2021, The Author(s).en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo18935
dc.identifier.doi10.1038/s41598-021-96872-w
dc.identifier.issue1
dc.identifier.scopus2-s2.0-85115394454
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85115394454&doi=10.1038%2fs41598-021-96872-w&partnerID=40&md5=f9996fb6bc6f396520cbf6eb411a9acc
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/25879
dc.identifier.volume11
dc.publisherNature Researchen_US
dc.relation.ispartofAll Open Access, Gold, Green
dc.sourceScopus
dc.sourcetitleScientific Reports
dc.titleDeveloping machine learning algorithms for meteorological temperature and humidity forecasting at Terengganu state in Malaysiaen_US
dc.typeArticleen_US
dspace.entity.typePublication
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