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A comparison of machine learning models for suspended sediment load classification

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dc.citedby5
dc.contributor.authorAlDahoul N.en_US
dc.contributor.authorAhmed A.N.en_US
dc.contributor.authorAllawi M.F.en_US
dc.contributor.authorSherif M.en_US
dc.contributor.authorSefelnasr A.en_US
dc.contributor.authorChau K.-W.en_US
dc.contributor.authorEl-Shafie A.en_US
dc.contributor.authorid56656478800en_US
dc.contributor.authorid57214837520en_US
dc.contributor.authorid57057678400en_US
dc.contributor.authorid7005414714en_US
dc.contributor.authorid6505592467en_US
dc.contributor.authorid7202674661en_US
dc.contributor.authorid16068189400en_US
dc.date.accessioned2023-05-29T09:40:44Z
dc.date.available2023-05-29T09:40:44Z
dc.date.issued2022
dc.description.abstractThe suspended sediment load (SSL) is one of the major hydrological processes affecting the sustainability of river planning and management. Moreover, sediments have a significant impact on dam operation and reservoir capacity. To this end, reliable and applicable models are required to compute and classify the SSL in rivers. The application of machine learning models has become common to solve complex problems such as SSL modeling. The present research investigated the ability of several models to classify the SSL data. This investigation aims to explore a new version of machine learning classifiers for SSL classification at Johor River, Malaysia. Extreme gradient boosting, random forest, support vector machine, multi-layer perceptron and k-nearest neighbors classifiers have been used to classify the SSL data. The sediment values are divided into multiple discrete ranges, where each range can be considered as one category or class. This study illustrates two different scenarios related to the number of categories, which are five and 10 categories, with two time scales, daily and weekly. The performance of the proposed models was evaluated by several statistical indicators. Overall, the proposed models achieved excellent classification of the SSL data under various scenarios. � 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1080/19942060.2022.2073565
dc.identifier.epage1232
dc.identifier.issue1
dc.identifier.scopus2-s2.0-85131082973
dc.identifier.spage1211
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85131082973&doi=10.1080%2f19942060.2022.2073565&partnerID=40&md5=12d87d57d37a067c1c7cd87ac65c01b8
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/27190
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.titleA comparison of machine learning models for suspended sediment load classificationen_US
dc.typeArticleen_US
dspace.entity.typePublication
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