Publication:
Streamflow classification by employing various machine learning models for peninsular Malaysia

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dc.citedby5
dc.contributor.authorAlDahoul N.en_US
dc.contributor.authorMomo M.A.en_US
dc.contributor.authorChong K.L.en_US
dc.contributor.authorAhmed A.N.en_US
dc.contributor.authorHuang Y.F.en_US
dc.contributor.authorSherif M.en_US
dc.contributor.authorEl-Shafie A.en_US
dc.contributor.authorid56656478800en_US
dc.contributor.authorid57441948400en_US
dc.contributor.authorid57208482172en_US
dc.contributor.authorid57214837520en_US
dc.contributor.authorid55807263900en_US
dc.contributor.authorid7005414714en_US
dc.contributor.authorid16068189400en_US
dc.date.accessioned2024-10-14T03:17:25Z
dc.date.available2024-10-14T03:17:25Z
dc.date.issued2023
dc.description.abstractDue to excessive streamflow (SF), Peninsular Malaysia has historically experienced floods and droughts. Forecasting streamflow to mitigate municipal and environmental damage is therefore crucial. Streamflow prediction has been extensively demonstrated in the literature to estimate the continuous values of streamflow level. Prediction of continuous values of streamflow is not necessary in several applications and at the same time it is very challenging task because of uncertainty. A streamflow category prediction is more advantageous for addressing the uncertainty in numerical point forecasting, considering that its predictions are linked to a propensity to belong to the pre-defined classes. Here, we formulate streamflow prediction as a time series classification with discrete ranges of values, each representing a class to classify streamflow into five or ten, respectively, using machine learning approaches in various rivers in Malaysia. The findings reveal that several models, specifically LSTM, outperform others in predicting the following n-time steps of streamflow because LSTM is able to learn the mapping between streamflow time series of 2 or 3�days ahead more than support vector machine (SVM) and gradient boosting (GB). LSTM produces higher F1 score in various rivers (by 5% in Johor, 2% in Kelantan and Melaka and Selangor, 4% in Perlis) in 2�days ahead scenario. Furthermore, the ensemble stacking of the SVM and GB achieves high performance in terms of F1 score and quadratic weighted kappa. Ensemble stacking gives 3% higher F1 score in Perak river compared to SVM and gradient boosting. � 2023, Springer Nature Limited.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo14574
dc.identifier.doi10.1038/s41598-023-41735-9
dc.identifier.issue1
dc.identifier.scopus2-s2.0-85169680347
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85169680347&doi=10.1038%2fs41598-023-41735-9&partnerID=40&md5=7baecf23595cf2b1edad649479d0d0f4
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/33907
dc.identifier.volume13
dc.publisherNature Researchen_US
dc.relation.ispartofAll Open Access
dc.relation.ispartofGold Open Access
dc.relation.ispartofGreen Open Access
dc.sourceScopus
dc.sourcetitleScientific Reports
dc.subjectarticle
dc.subjectforecasting
dc.subjectJohor
dc.subjectKelantan
dc.subjectmachine learning
dc.subjectMalaysia
dc.subjectMelaka
dc.subjectPerlis
dc.subjectprediction
dc.subjectriver
dc.subjectSelangor
dc.subjectsupport vector machine
dc.subjecttime series analysis
dc.subjectuncertainty
dc.titleStreamflow classification by employing various machine learning models for peninsular Malaysiaen_US
dc.typeArticleen_US
dspace.entity.typePublication
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