An inclusive multiple model for predicting total sediment transport rate in the presence of coastal vegetation cover based on optimized kernel extreme learning models
coenzyme A; algorithm; learning; Algorithms; Coenzyme A; Learning
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Springer Science and Business Media Deutschland GmbH
2023
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my.uniten.dspace-272022023-05-29T17:40:54Z An inclusive multiple model for predicting total sediment transport rate in the presence of coastal vegetation cover based on optimized kernel extreme learning models Jalil-Masir H. Fattahi R. Ghanbari-Adivi E. Asadi Aghbolaghi M. Ehteram M. Ahmed A.N. El-Shafie A. 57224857052 57224862062 57222383988 15724626600 57113510800 57214837520 16068189400 coenzyme A; algorithm; learning; Algorithms; Coenzyme A; Learning Predicting 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. Article in Press 2023-05-29T09:40:54Z 2023-05-29T09:40:54Z 2022 Article 10.1007/s11356-022-20472-y 2-s2.0-85129510791 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85129510791&doi=10.1007%2fs11356-022-20472-y&partnerID=40&md5=393c8c6bfaf05d730203bb9976885302 https://irepository.uniten.edu.my/handle/123456789/27202 Springer Science and Business Media Deutschland GmbH Scopus |
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coenzyme A; algorithm; learning; Algorithms; Coenzyme A; Learning |
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57224857052 Jalil-Masir H. Fattahi R. Ghanbari-Adivi E. Asadi Aghbolaghi M. Ehteram M. Ahmed A.N. El-Shafie A. |
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Jalil-Masir H. Fattahi R. Ghanbari-Adivi E. Asadi Aghbolaghi M. Ehteram M. Ahmed A.N. El-Shafie A. |
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Jalil-Masir H. Fattahi R. Ghanbari-Adivi E. Asadi Aghbolaghi M. Ehteram M. Ahmed A.N. El-Shafie A. An inclusive multiple model for predicting total sediment transport rate in the presence of coastal vegetation cover based on optimized kernel extreme learning models |
| author_sort |
Jalil-Masir H. |
| title |
An inclusive multiple model for predicting total sediment transport rate in the presence of coastal vegetation cover based on optimized kernel extreme learning models |
| title_short |
An inclusive multiple model for predicting total sediment transport rate in the presence of coastal vegetation cover based on optimized kernel extreme learning models |
| title_full |
An inclusive multiple model for predicting total sediment transport rate in the presence of coastal vegetation cover based on optimized kernel extreme learning models |
| title_fullStr |
An inclusive multiple model for predicting total sediment transport rate in the presence of coastal vegetation cover based on optimized kernel extreme learning models |
| title_full_unstemmed |
An inclusive multiple model for predicting total sediment transport rate in the presence of coastal vegetation cover based on optimized kernel extreme learning models |
| title_sort |
inclusive multiple model for predicting total sediment transport rate in the presence of coastal vegetation cover based on optimized kernel extreme learning models |
| publisher |
Springer Science and Business Media Deutschland GmbH |
| publishDate |
2023 |
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1806428176389242880 |
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13.211869 |