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An inclusive multiple model for predicting total sediment transport rate in the presence of coastal vegetation cover based on optimized kernel extreme learning models

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dc.citedby4
dc.contributor.authorJalil-Masir H.en_US
dc.contributor.authorFattahi R.en_US
dc.contributor.authorGhanbari-Adivi E.en_US
dc.contributor.authorAsadi Aghbolaghi M.en_US
dc.contributor.authorEhteram M.en_US
dc.contributor.authorAhmed A.N.en_US
dc.contributor.authorEl-Shafie A.en_US
dc.contributor.authorid57224857052en_US
dc.contributor.authorid57224862062en_US
dc.contributor.authorid57222383988en_US
dc.contributor.authorid15724626600en_US
dc.contributor.authorid57113510800en_US
dc.contributor.authorid57214837520en_US
dc.contributor.authorid16068189400en_US
dc.date.accessioned2023-05-29T09:40:54Z
dc.date.available2023-05-29T09:40:54Z
dc.date.issued2022
dc.descriptioncoenzyme A; algorithm; learning; Algorithms; Coenzyme A; Learningen_US
dc.description.abstractPredicting sediment transport rate (STR) in the presence of flexible vegetation is a critical task for modelers. Sediment transport modeling methods in the coastal region is equally challenging due to the nonlinearity of the STR�vegetation interaction. In the present study, the kernel extreme learning model (KELM) was integrated with the seagull optimization algorithm (SEOA), the crow optimization algorithm (COA), the firefly algorithm (FFA), and particle swarm optimization (PSO) to estimate the STR in the presence of vegetation cover. The rigidity index, D50/wave height, Newton number, drag coefficient, and cover density were used as inputs to the models. The root mean square error (RMSE), the mean absolute error (MAE), and percentage of bias (PBIAS) were used to evaluate the capability of models. This study applied the novel ensemble model, and the inclusive multiple model (IMM), to assemble the outputs of the KELM models. In addition, the innovations of this study were the introduction of a new IMM model, and the use of new hybrid KELM models for predicting STR and investigating the effects of various parameters on the STR. At the testing level, the MAE of the IMM model was 22, 60, 68, 73, and 76% lower than those of the KELM-SEOA, KELM-COA, KELM-PSO, and KELM models, respectively. The IMM had a PBIAS of 5, whereas the KELM-SEOA, KELM-COA, KELM-PSOA, and KELM had PBIAS of 9, 12, 14, 18, and 21%, respectively. The results indicated that the increasing drag coefficient and D50/wave height had decreased the STR. From the findings, it was revealed that the IMM and KELM-SEOA had higher predictive ability for STR. Since the sediment is one of the most important sources of environmental pollution, therefore, this study is useful for monitoring and controlling environmental pollution. � 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.en_US
dc.description.natureArticle in Pressen_US
dc.identifier.doi10.1007/s11356-022-20472-y
dc.identifier.scopus2-s2.0-85129510791
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85129510791&doi=10.1007%2fs11356-022-20472-y&partnerID=40&md5=393c8c6bfaf05d730203bb9976885302
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/27202
dc.publisherSpringer Science and Business Media Deutschland GmbHen_US
dc.sourceScopus
dc.sourcetitleEnvironmental Science and Pollution Research
dc.titleAn inclusive multiple model for predicting total sediment transport rate in the presence of coastal vegetation cover based on optimized kernel extreme learning modelsen_US
dc.typeArticleen_US
dspace.entity.typePublication
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