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Pipeline scour rates prediction-based model utilizing a multilayer perceptron-colliding body algorithm

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dc.citedby19
dc.contributor.authorEhteram M.en_US
dc.contributor.authorAhmed A.N.en_US
dc.contributor.authorLing L.en_US
dc.contributor.authorFai C.M.en_US
dc.contributor.authorLatif S.D.en_US
dc.contributor.authorAfan H.A.en_US
dc.contributor.authorBanadkooki F.B.en_US
dc.contributor.authorEl-Shafie A.en_US
dc.contributor.authorid57113510800en_US
dc.contributor.authorid57214837520en_US
dc.contributor.authorid56203785300en_US
dc.contributor.authorid57214146115en_US
dc.contributor.authorid57216081524en_US
dc.contributor.authorid56436626600en_US
dc.contributor.authorid57201068611en_US
dc.contributor.authorid16068189400en_US
dc.date.accessioned2023-05-29T08:10:50Z
dc.date.available2023-05-29T08:10:50Z
dc.date.issued2020
dc.descriptionForecasting; Multilayers; Particle swarm optimization (PSO); Pipelines; Soft computing; Colliding bodies; MLP model; Multi layer perceptron; Optimization algorithms; Optimization modeling; Prediction model; Soft computing models; Wave characteristics; Scour; algorithm; hydrological modeling; model; optimization; pipeline; scour; Cetaceaen_US
dc.description.abstractIn this research, the advanced multilayer perceptron (MLP) models are utilized to predict the free rate of expansion that usually occurs around the pipeline (PL) because of waves. The MLP model was structured by integrating it with three optimization algorithms: particle swarm optimization (PSO), whale algorithm (WA), and colliding bodies' optimization (CBO). The sediment size, wave characteristics, and PL geometry were used as the inputs for the applied models. Moreover, the scour rate, vertical scour rate along the pipeline, and scour rate at both right and left sides of the pipeline were predicted as the model outputs. Results of the three suggested models, MLP-CBO, MLP-WA, and MLP-PSO, for both testing and training sessions were assessed based on different statistical indices. The results indicated that the MLP-CBO model performed better in comparison to the MLP-PSO, MLP-WA, regression, and empirical models. The MLP-CBO can be used as a powerful soft-computing model for predictions. � 2020 by the authors.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo902
dc.identifier.doi10.3390/w12030902
dc.identifier.issue3
dc.identifier.scopus2-s2.0-85082559358
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85082559358&doi=10.3390%2fw12030902&partnerID=40&md5=871ded5aadbf59645df2e3d110c3b80f
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/25555
dc.identifier.volume12
dc.publisherMDPI AGen_US
dc.relation.ispartofAll Open Access, Gold
dc.sourceScopus
dc.sourcetitleWater (Switzerland)
dc.titlePipeline scour rates prediction-based model utilizing a multilayer perceptron-colliding body algorithmen_US
dc.typeArticleen_US
dspace.entity.typePublication
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