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A Performance Comparison of Various Artificial Intelligence Approaches for Estimation of Sediment of River Systems

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dc.citedby2
dc.contributor.authorHayder G.en_US
dc.contributor.authorSolihin M.I.en_US
dc.contributor.authorKushiar K.F.B.en_US
dc.contributor.authorid56239664100en_US
dc.contributor.authorid16644075500en_US
dc.contributor.authorid57212462702en_US
dc.date.accessioned2023-05-29T09:11:28Z
dc.date.available2023-05-29T09:11:28Z
dc.date.issued2021
dc.description.abstractSediment is a universal issue that is generated in the river catchment and affects the river flow, reservoir capacity, hydropower generation and dam structure. This paper aims to present the result of experimentation in sediment load estimation using various machine learning algorithms as a powerful AI approach. The data was collected from eight locations in upstream area of Ringlet reservoir catchment. The input variables are discharge and suspended solid. It was found that there is strong correlation between sediment and suspended solid with correlation coefficient of R = 0.9. The developed ML model successfully estimated the sediment load with competitive results from ANN, Decision Tree, AdaBoost and SVM. The best result was produced by SVM (v-SVM version) where very low RMSE was generated for both training and testing dataset despite its more complicated hyperparameters setup. The results also show a promising application of machine learning for future prediction in hydro-informatic systems. � 2021, Journal of Ecological Engineering. All Rights Reserved.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.12911/22998993/137847
dc.identifier.epage27
dc.identifier.issue7
dc.identifier.scopus2-s2.0-85110556488
dc.identifier.spage20
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85110556488&doi=10.12911%2f22998993%2f137847&partnerID=40&md5=92759d91d61d0387b84a98c3a1ab1748
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/26521
dc.identifier.volume22
dc.publisherPolish Society of Ecological Engineering (PTIE)en_US
dc.relation.ispartofAll Open Access, Gold
dc.sourceScopus
dc.sourcetitleJournal of Ecological Engineering
dc.titleA Performance Comparison of Various Artificial Intelligence Approaches for Estimation of Sediment of River Systemsen_US
dc.typeArticleen_US
dspace.entity.typePublication
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