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Predicting longitudinal dispersion coefficient using ensemble models and optimized multi-layer perceptron models

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dc.citedby2
dc.contributor.authorGholami M.en_US
dc.contributor.authorGhanbari-Adivi E.en_US
dc.contributor.authorEhteram M.en_US
dc.contributor.authorSingh V.P.en_US
dc.contributor.authorNajah Ahmed A.en_US
dc.contributor.authorMosavi A.en_US
dc.contributor.authorEl-Shafie A.en_US
dc.contributor.authorid56973673400en_US
dc.contributor.authorid57222383988en_US
dc.contributor.authorid57113510800en_US
dc.contributor.authorid57211219633en_US
dc.contributor.authorid58136810800en_US
dc.contributor.authorid57191408081en_US
dc.contributor.authorid16068189400en_US
dc.date.accessioned2024-10-14T03:17:25Z
dc.date.available2024-10-14T03:17:25Z
dc.date.issued2023
dc.description.abstractPrediction of the longitudinal dispersion coefficient (LDC) is essential for the river and water resources engineering and environmental management. This study proposes ensemble models for predicting LDC based on multilayer perceptron (MULP) methods and optimization algorithms. The honey badger optimization algorithm (HBOA), salp swarm algorithm (SASA), firefly algorithm (FIFA), and particle swarm optimization algorithm (PASOA) are used to adjust the MULP parameters. Then, the outputs of the MULP-HBOA, MULP-SASA, MULP-PASOA, MULP-FIFA, and MULP models were incorporated into an inclusive multiple model (IMM). For IMM at the testing level, the mean absolute error (MEAE) was 15, whereas it was 17, 18, 23, 24, and 25 for the MULP-HBOA, MULP-SASA, MULP-FIFA, MULP-PASOA, and MULP models. The study also modified the structure of MULP models using a goodness factor which decreased the CPU time. Removing redundant neurons reduces CPU time. Thus, the modified ANN model and the suggested IMM model can decrease the computational time and further improve the performance of models. � 2023 THE AUTHORSen_US
dc.description.natureFinalen_US
dc.identifier.ArtNo102223
dc.identifier.doi10.1016/j.asej.2023.102223
dc.identifier.issue12
dc.identifier.scopus2-s2.0-85149766266
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85149766266&doi=10.1016%2fj.asej.2023.102223&partnerID=40&md5=aeede0035fc62f36fd7c491aaa9f15e7
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/33906
dc.identifier.volume14
dc.publisherAin Shams Universityen_US
dc.relation.ispartofAll Open Access
dc.relation.ispartofGold Open Access
dc.sourceScopus
dc.sourcetitleAin Shams Engineering Journal
dc.subjectArtificial intelligence
dc.subjectBig data
dc.subjectDeep learning
dc.subjectLongitudinal dispersion coefficient
dc.subjectMachine learning
dc.subjectMultilayer perceptron
dc.subjectOptimization
dc.subjectBig data
dc.subjectDeep learning
dc.subjectDispersions
dc.subjectEnvironmental management
dc.subjectForecasting
dc.subjectLearning systems
dc.subjectMultilayer neural networks
dc.subjectMultilayers
dc.subjectWater resources
dc.subjectDeep learning
dc.subjectFirefly algorithms
dc.subjectLongitudinal dispersion coefficient
dc.subjectMachine-learning
dc.subjectMultilayers perceptrons
dc.subjectOptimisations
dc.subjectOptimization algorithms
dc.subjectParticle swarm optimization algorithm
dc.subjectSalp swarms
dc.subjectSwarm algorithms
dc.subjectParticle swarm optimization (PSO)
dc.titlePredicting longitudinal dispersion coefficient using ensemble models and optimized multi-layer perceptron modelsen_US
dc.typeArticleen_US
dspace.entity.typePublication
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