Publication:
Enhancing sediment transport predictions through machine learning-based multi-scenario regression models

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dc.citedby11
dc.contributor.authorAbid Almubaidin M.A.en_US
dc.contributor.authorLatif S.D.en_US
dc.contributor.authorBalan K.en_US
dc.contributor.authorAhmed A.N.en_US
dc.contributor.authorEl-Shafie A.en_US
dc.contributor.authorid58729517300en_US
dc.contributor.authorid57216081524en_US
dc.contributor.authorid58729125400en_US
dc.contributor.authorid57214837520en_US
dc.contributor.authorid16068189400en_US
dc.date.accessioned2024-10-14T03:17:20Z
dc.date.available2024-10-14T03:17:20Z
dc.date.issued2023
dc.description.abstractMachine learning is one effective way of increasing the accuracy of sediment transport models. Machine learning captures patterns in the sequence of both structured and unstructured data and uses it for forecasting. In this research, the different regression models were train to forecast sediment data using 8 years of measured sediment data collected in Sg. Linggui suspended sediment station. Data from different scenarios were used where each scenario indicates the number of lags. Seven regression models, namely, Linear Regression, Regression Trees, Support Vector Machines, Gaussian Process Regression, Kernel Approximation, Ensemble of Trees, and Neural Network were trained using the data and compared. The trained models were evaluated using Root Mean Square Error (RMSE) and Coefficient of Determination (R2). The best-performing models from two different types of regression models were chosen and they were tested using the test data to find the Relative Percentage Error (RPE) of the predicted data. The Exponential Gaussian Process Regression model performs much better than the other models in terms of RMSE and R2 values. When the exponential models from all 3 scenarios are compared, scenario 3 seems to have a better-performing model but only by a very small margin, after using testing data, the result shows scenario 3 has less RPE compared to other models. Hence, it can be deduced that the exponential gaussian process regression model from scenario 3 is the best-performing model overall in terms of RSME, R2, and RPE. � 2023en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo101585
dc.identifier.doi10.1016/j.rineng.2023.101585
dc.identifier.scopus2-s2.0-85178166466
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85178166466&doi=10.1016%2fj.rineng.2023.101585&partnerID=40&md5=cc27c78e558cddf089fc77ef35a0146a
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/33845
dc.identifier.volume20
dc.publisherElsevier B.V.en_US
dc.relation.ispartofAll Open Access
dc.relation.ispartofGold Open Access
dc.sourceScopus
dc.sourcetitleResults in Engineering
dc.subjectediment transport modelling
dc.subjectEnsemble of trees
dc.subjectGaussian process regression
dc.subjectKernel approximation
dc.subjectLinear regression
dc.subjectRegression trees
dc.subjectSupport vector machines
dc.subjectForecasting
dc.subjectGaussian distribution
dc.subjectGaussian noise (electronic)
dc.subjectLearning systems
dc.subjectLinear regression
dc.subjectMean square error
dc.subjectSediment transport
dc.subjectSedimentation
dc.subjectSuspended sediments
dc.subjectEdiment transport modeling
dc.subjectEnsemble of tree
dc.subjectGaussian process regression
dc.subjectKernel approximation
dc.subjectMachine-learning
dc.subjectPercentage error
dc.subjectRegression modelling
dc.subjectRegression trees
dc.subjectSupport vectors machine
dc.subjectTransport modelling
dc.subjectSupport vector machines
dc.titleEnhancing sediment transport predictions through machine learning-based multi-scenario regression modelsen_US
dc.typeArticleen_US
dspace.entity.typePublication
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