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Suspended sediment load prediction using long short-term memory neural network

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dc.citedby29
dc.contributor.authorAlDahoul N.en_US
dc.contributor.authorEssam Y.en_US
dc.contributor.authorKumar P.en_US
dc.contributor.authorAhmed A.N.en_US
dc.contributor.authorSherif M.en_US
dc.contributor.authorSefelnasr A.en_US
dc.contributor.authorElshafie A.en_US
dc.contributor.authorid56656478800en_US
dc.contributor.authorid57203146903en_US
dc.contributor.authorid57206939156en_US
dc.contributor.authorid57214837520en_US
dc.contributor.authorid7005414714en_US
dc.contributor.authorid6505592467en_US
dc.contributor.authorid16068189400en_US
dc.date.accessioned2023-05-29T09:05:27Z
dc.date.available2023-05-29T09:05:27Z
dc.date.issued2021
dc.descriptionarticle; Johor; linear regression analysis; long short term memory network; multilayer perceptron; particle resuspension; prediction; riveren_US
dc.description.abstractRivers carry suspended sediments along with their flow. These sediments deposit at different places depending on the discharge and course of the river. However, the deposition of these sediments impacts environmental health, agricultural activities, and portable water sources. Deposition of suspended sediments reduces the flow area, thus affecting the movement of aquatic lives and ultimately leading to the change of river course. Thus, the data of suspended sediments and their variation is crucial information for various authorities. Various authorities require the forecasted data of suspended sediments in the river to operate various hydraulic structures properly. Usually, the prediction of suspended sediment concentration (SSC) is challenging due to various factors, including site-related data, site-related modelling, lack of multiple observed factors used for prediction, and pattern complexity.Therefore, to address previous problems, this study proposes a Long Short Term Memory model to predict suspended sediments in Malaysia's Johor River utilizing only one observed factor, including discharge data. The data was collected for the period of 1988�1998. Four different models were tested, in this study, for the prediction of suspended sediments, which are: ElasticNet Linear Regression (L.R.), Multi-Layer Perceptron (MLP) neural network, Extreme Gradient Boosting, and Long Short-Term Memory. Predictions were analysed based on four different scenarios such as daily, weekly, 10-daily, and monthly. Performance evaluation stated that Long Short-Term Memory outperformed other models with the regression values of 92.01%, 96.56%, 96.71%, and 99.45% daily, weekly, 10-days, and monthly scenarios, respectively. � 2021, The Author(s).en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo7826
dc.identifier.doi10.1038/s41598-021-87415-4
dc.identifier.issue1
dc.identifier.scopus2-s2.0-85104157867
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85104157867&doi=10.1038%2fs41598-021-87415-4&partnerID=40&md5=236aeb317a5c82d1db499a27177d2724
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/25893
dc.identifier.volume11
dc.publisherNature Researchen_US
dc.relation.ispartofAll Open Access, Gold, Green
dc.sourceScopus
dc.sourcetitleScientific Reports
dc.titleSuspended sediment load prediction using long short-term memory neural networken_US
dc.typeArticleen_US
dspace.entity.typePublication
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