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Predicting streamflow in Peninsular Malaysia using support vector machine and deep learning algorithms

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dc.citedby9
dc.contributor.authorEssam Y.en_US
dc.contributor.authorHuang Y.F.en_US
dc.contributor.authorNg J.L.en_US
dc.contributor.authorBirima A.H.en_US
dc.contributor.authorAhmed A.N.en_US
dc.contributor.authorEl-Shafie A.en_US
dc.contributor.authorid57203146903en_US
dc.contributor.authorid55807263900en_US
dc.contributor.authorid57192698412en_US
dc.contributor.authorid23466519000en_US
dc.contributor.authorid57214837520en_US
dc.contributor.authorid16068189400en_US
dc.date.accessioned2023-05-29T09:36:05Z
dc.date.available2023-05-29T09:36:05Z
dc.date.issued2022
dc.descriptionalgorithm; Malaysia; river; support vector machine; Algorithms; Deep Learning; Malaysia; Rivers; Support Vector Machineen_US
dc.description.abstractFloods and droughts are environmental phenomena that occur in Peninsular Malaysia due to extreme values of streamflow (SF). Due to this, the study of SF prediction is highly significant for the purpose of municipal and environmental damage mitigation. In the present study, machine learning (ML) models based on the support vector machine (SVM), artificial neural network (ANN), and long short-term memory (LSTM), are tested and developed to predict SF for 11 different rivers throughout Peninsular Malaysia. SF data sets for the rivers were collected from the Malaysian Department of Irrigation and Drainage. The main objective of the present study is to propose a universal model that is most capable of predicting SFs for rivers within Peninsular Malaysia. Based on the findings, the ANN3 model which was developed using the ANN algorithm and input scenario 3 (inputs consisting of previous 3�days SF) is deduced as the best overall ML model for SF prediction as it outperformed all the other models in 4 out of 11 of the tested data sets; and obtained among the highest average RMs with a score of 3.27, hence indicating that the model is very adaptable and reliable in accurately predicting SF based on different data sets and river case studies. Therefore, the ANN3 model is proposed as a universal model for SF prediction within Peninsular Malaysia. � 2022, The Author(s).en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo3883
dc.identifier.doi10.1038/s41598-022-07693-4
dc.identifier.issue1
dc.identifier.scopus2-s2.0-85126218963
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85126218963&doi=10.1038%2fs41598-022-07693-4&partnerID=40&md5=4157393c5cd5160aff4b650599dcfd58
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/26661
dc.identifier.volume12
dc.publisherNature Researchen_US
dc.relation.ispartofAll Open Access, Gold, Green
dc.sourceScopus
dc.sourcetitleScientific Reports
dc.titlePredicting streamflow in Peninsular Malaysia using support vector machine and deep learning algorithmsen_US
dc.typeArticleen_US
dspace.entity.typePublication
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