Publication:
River Water Suspended Sediment Predictive Analytics Using Artificial Neural Network and Convolutional Neural Network Approach: A Review

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dc.citedby4
dc.contributor.authorKhan Q.en_US
dc.contributor.authorHayder G.en_US
dc.contributor.authorAl-Zwainy F.M.S.en_US
dc.contributor.authorid58309988500en_US
dc.contributor.authorid56239664100en_US
dc.contributor.authorid55347693000en_US
dc.date.accessioned2024-10-14T03:21:10Z
dc.date.available2024-10-14T03:21:10Z
dc.date.issued2023
dc.description.abstractFor water resource management and water quality challenges, estimating suspended sediment is crucial. It demands accurate data and information on suspended sediment concentrations (SSC). Because real sampling can be difficult during severe weather and certain old approaches will not yield enough data, engineers are developing new accurate forecasting technologies. The aim of this study is to see if machine learning techniques like convolutional neural network (CNN) and artificial neural network (ANN) could be utilized to estimate SSC in Malaysia�s Langat stream. The CNN is a form of machine learning method that has not gotten much attention around SSC prediction. The prediction model created in this work is intended for use in the water quality monitoring of Langat stream in Malaysia. River discharge and suspended solids will be the input variables for the models. Both models will be analyzed using the three criteria for performance including root mean square error (RMSE), coefficient of determination (R2), and mean absolute error (MAE) to find which is more accurate in predicting the SSC for this river. The model with the top performance will have the lowest MAE and RMSE values as well as the high value of R2. This study will contribute to demonstrating how machine learning may be used to forecast future suspended sediment concentrations in rivers. � 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1007/978-3-031-26580-8_10
dc.identifier.epage56
dc.identifier.scopus2-s2.0-85161606770
dc.identifier.spage51
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85161606770&doi=10.1007%2f978-3-031-26580-8_10&partnerID=40&md5=c84a8c2cabc8d9a291e2de1b7e73fa8f
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/34621
dc.pagecount5
dc.publisherSpringer Natureen_US
dc.sourceScopus
dc.sourcetitleAdvances in Science, Technology and Innovation
dc.subjectANN
dc.subjectCNN
dc.subjectLangat river
dc.subjectMAE
dc.subjectR<sup>2</sup>
dc.subjectRMSE
dc.subjectSuspended sediment
dc.titleRiver Water Suspended Sediment Predictive Analytics Using Artificial Neural Network and Convolutional Neural Network Approach: A Reviewen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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