Publication:
Water level prediction using various machine learning algorithms: a case study of Durian Tunggal river, Malaysia

dc.citedby5
dc.contributor.authorAhmed A.N.en_US
dc.contributor.authorYafouz A.en_US
dc.contributor.authorBirima A.H.en_US
dc.contributor.authorKisi O.en_US
dc.contributor.authorHuang Y.F.en_US
dc.contributor.authorSherif M.en_US
dc.contributor.authorSefelnasr A.en_US
dc.contributor.authorEl-Shafie A.en_US
dc.contributor.authorid57214837520en_US
dc.contributor.authorid57221981418en_US
dc.contributor.authorid23466519000en_US
dc.contributor.authorid6507051085en_US
dc.contributor.authorid55807263900en_US
dc.contributor.authorid7005414714en_US
dc.contributor.authorid6505592467en_US
dc.contributor.authorid16068189400en_US
dc.date.accessioned2023-05-29T09:41:47Z
dc.date.available2023-05-29T09:41:47Z
dc.date.issued2022
dc.description.abstractA reliable model to predict the changes in the water levels in a river is crucial for better planning to mitigate any risk associated with flooding. In this study, six different Machine Learning (ML) algorithms were developed to predict the river�s water level, on a daily basis based on collected data from 1990 to 2019 which were used to train and test the proposed models. Different input combinations were explored to improve the accuracy of the model. Statistical indicators were calculated to examine the reliability of the proposed models with other models. The comparison of several data-driven regression methods indicate that the exponential Gaussian Process Regression (GPR) model offered better accuracy in predicting daily water levels with respect to different assessment criteria. The GPR model was then used to predict the water level after sorting the data based on 10 days maximum and minimum values of the water level, and the results proved the success of this model in catching the extremes of the water levels. In addition to that, based on two uncertainty indicators, it was concluded that the proposed model, the GPR, was capable of predicting the water level of the river with high precision and less uncertainty where the computed using the 95% prediction uncertainty (95PPU) and the d-factor were found to be equal to 98.276 and 0.000525, respectively. The findings of this study show the efficacy of the GPR model in capturing the changes in the water level in a river. Due to the importance of the water level of a river being an parameter for flood monitoring, this technique is likely beneficial to the design of the mitigation strategies for future flooding events. � 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1080/19942060.2021.2019128
dc.identifier.epage440
dc.identifier.issue1
dc.identifier.scopus2-s2.0-85124294502
dc.identifier.spage422
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85124294502&doi=10.1080%2f19942060.2021.2019128&partnerID=40&md5=89daaf472096687e48483df97d367b28
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/27263
dc.identifier.volume16
dc.publisherTaylor and Francis Ltd.en_US
dc.relation.ispartofAll Open Access, Gold
dc.sourceScopus
dc.sourcetitleEngineering Applications of Computational Fluid Mechanics
dc.titleWater level prediction using various machine learning algorithms: a case study of Durian Tunggal river, Malaysiaen_US
dc.typeArticleen_US
dspace.entity.typePublication
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