Publication:
Sediment load prediction in Johor river: deep learning versus machine learning models

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dc.citedby27
dc.contributor.authorLatif S.D.en_US
dc.contributor.authorChong K.L.en_US
dc.contributor.authorAhmed A.N.en_US
dc.contributor.authorHuang Y.F.en_US
dc.contributor.authorSherif M.en_US
dc.contributor.authorEl-Shafie A.en_US
dc.contributor.authorid57216081524en_US
dc.contributor.authorid57208482172en_US
dc.contributor.authorid57214837520en_US
dc.contributor.authorid55807263900en_US
dc.contributor.authorid7005414714en_US
dc.contributor.authorid16068189400en_US
dc.date.accessioned2024-10-14T03:18:54Z
dc.date.available2024-10-14T03:18:54Z
dc.date.issued2023
dc.description.abstractSediment transport is a normal phenomenon in rivers and streams, contributing significantly to ecosystem production and preservation by replenishing vital nutrients and preserving aquatic life�s natural habitats. Thus, sediment transport prediction through modeling is crucial for predicting flood events, tracking coastal erosion, planning for water supplies, and managing irrigation. The predictability of process-driven models may encounter various restrictions throughout the validation process. Given that data-driven models work on the assumption that the underlying physical process is not requisite, this opens up the avenue for AI-based model as alternative modeling. However, AI-based models, such as ANN and SVM, face problems, such as long-term dependency, which require alternative dynamic procedures. Since their performance as universal function approximation depends on their compatibility with the nature of the problem itself, this study investigated several distinct AI-based models, such as long short-term memory (LSTM), artificial neural network (ANN), and support vector machine (SVM), in predicting sediment transport in the Johor river. The collected historical daily sediment transport data from January 1, 2008, to December 01, 2018, through autocorrelation function, were used as input for the model. The statistical results showed that, despite their ability (deep learning and machine learning) to provide sediment predictions based on historical input datasets, machine learning, such as ANN, might be more prone to overfitting or being trapped in a local optimum than deep learning, evidenced by the worse in all metrics score. With RMSE = 11.395, MAE = 18.094, and R2 = 0.914, LSTM outperformed other models in the comparison. � 2023, The Author(s).en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo79
dc.identifier.doi10.1007/s13201-023-01874-w
dc.identifier.issue3
dc.identifier.scopus2-s2.0-85148426283
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85148426283&doi=10.1007%2fs13201-023-01874-w&partnerID=40&md5=5f66adcb1daf4fa99c9cc5fce93beb6f
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/34298
dc.identifier.volume13
dc.publisherSpringer Science and Business Media Deutschland GmbHen_US
dc.relation.ispartofAll Open Access
dc.relation.ispartofGold Open Access
dc.sourceScopus
dc.sourcetitleApplied Water Science
dc.subjectArtificial neural network (ANN)
dc.subjectLong short-term memory (LSTM)
dc.subjectSediment transport prediction
dc.subjectSupport vector machine (SVM)
dc.subjectJohor
dc.subjectJohor River
dc.subjectMalaysia
dc.subjectWest Malaysia
dc.subjectBrain
dc.subjectFloods
dc.subjectForecasting
dc.subjectLearning systems
dc.subjectLong short-term memory
dc.subjectRivers
dc.subjectSediment transport
dc.subjectWater supply
dc.subjectArtificial neural network
dc.subjectLoad predictions
dc.subjectLong short-term memory
dc.subjectMachine learning models
dc.subjectMachine-learning
dc.subjectNeural network and support vector machines
dc.subjectSediment loads
dc.subjectSediment transport prediction
dc.subjectSupport vector machine
dc.subjectSupport vectors machine
dc.subjectartificial neural network
dc.subjectautocorrelation
dc.subjectcomparative study
dc.subjectcomputer simulation
dc.subjectfluvial deposit
dc.subjectmachine learning
dc.subjectmodel validation
dc.subjectnumerical model
dc.subjectprediction
dc.subjectsediment transport
dc.subjectsupport vector machine
dc.subjectSupport vector machines
dc.titleSediment load prediction in Johor river: deep learning versus machine learning modelsen_US
dc.typeArticleen_US
dspace.entity.typePublication
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