Performance evaluation of sediment ejector efficiency using hybrid neuro-fuzzy models
Sediment transport in the ejector is highly stochastic and non-linear in nature, and its accurate estimation is a complex and challenging mission. This study attempts to investigate the sediment removal estimation of sediment ejector using newly developed hybrid data-intelligence models. The propose...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2021-01-01
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| Series: | Engineering Applications of Computational Fluid Mechanics |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/19942060.2021.1893224 |
| _version_ | 1818754739618185216 |
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| author | Ahmad Sharafati Masoud Haghbin Nand Kumar Tiwari Suraj Kumar Bhagat Nadhir Al-Ansari Kwok-Wing Chau Zaher Mundher Yaseen |
| author_facet | Ahmad Sharafati Masoud Haghbin Nand Kumar Tiwari Suraj Kumar Bhagat Nadhir Al-Ansari Kwok-Wing Chau Zaher Mundher Yaseen |
| author_sort | Ahmad Sharafati |
| collection | DOAJ |
| description | Sediment transport in the ejector is highly stochastic and non-linear in nature, and its accurate estimation is a complex and challenging mission. This study attempts to investigate the sediment removal estimation of sediment ejector using newly developed hybrid data-intelligence models. The proposed models are based on the hybridization of adaptive neuro-fuzzy inference systems (ANFIS) with different metaheuristic algorithms, namely, particle swarm optimization (PSO), genetic algorithm (GA), differential evolution (DE), and ant colony optimization (ACO). The proposed models are constructed with various related input variables such as sediment concentration, flow depth, velocity, sediment size, Froude number, extraction ratio, number of tunnels and sub-tunnels, and flow depth at upstream of the sediment ejector. The estimation capacity of the developed hybrid models is assessed using several statistical evaluation indices. The modeling results obtained for the studied ejector sediment removal estimation demonstrated an optimistic finding. Among the developed hybrid models, ANFIS-PSO model exhibited the best predictability potential with maximum correlation coefficient values CC Train = 0.915 and CCTest = 0.916. |
| first_indexed | 2024-12-18T05:28:02Z |
| format | Article |
| id | doaj.art-c1c996507b99451db9859d964596937f |
| institution | Directory Open Access Journal |
| issn | 1994-2060 1997-003X |
| language | English |
| last_indexed | 2024-12-18T05:28:02Z |
| publishDate | 2021-01-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Engineering Applications of Computational Fluid Mechanics |
| spelling | doaj.art-c1c996507b99451db9859d964596937f2022-12-21T21:19:30ZengTaylor & Francis GroupEngineering Applications of Computational Fluid Mechanics1994-20601997-003X2021-01-0115162764310.1080/19942060.2021.18932241893224Performance evaluation of sediment ejector efficiency using hybrid neuro-fuzzy modelsAhmad Sharafati0Masoud Haghbin1Nand Kumar Tiwari2Suraj Kumar Bhagat3Nadhir Al-Ansari4Kwok-Wing Chau5Zaher Mundher Yaseen6Science and Research Branch, Islamic Azad UniversityUniversity of GranadaNational Institute of TechnologyTon Duc Thang UniversityLuleå University of TechnologyHong Kong Polytechnic UniversityScientific Research Center, Al-Ayen UniversitySediment transport in the ejector is highly stochastic and non-linear in nature, and its accurate estimation is a complex and challenging mission. This study attempts to investigate the sediment removal estimation of sediment ejector using newly developed hybrid data-intelligence models. The proposed models are based on the hybridization of adaptive neuro-fuzzy inference systems (ANFIS) with different metaheuristic algorithms, namely, particle swarm optimization (PSO), genetic algorithm (GA), differential evolution (DE), and ant colony optimization (ACO). The proposed models are constructed with various related input variables such as sediment concentration, flow depth, velocity, sediment size, Froude number, extraction ratio, number of tunnels and sub-tunnels, and flow depth at upstream of the sediment ejector. The estimation capacity of the developed hybrid models is assessed using several statistical evaluation indices. The modeling results obtained for the studied ejector sediment removal estimation demonstrated an optimistic finding. Among the developed hybrid models, ANFIS-PSO model exhibited the best predictability potential with maximum correlation coefficient values CC Train = 0.915 and CCTest = 0.916.http://dx.doi.org/10.1080/19942060.2021.1893224sediment ejectoradaptive neuro-fuzzy inference systemshybrid modelsediment removal efficiencymetaheuristic models |
| spellingShingle | Ahmad Sharafati Masoud Haghbin Nand Kumar Tiwari Suraj Kumar Bhagat Nadhir Al-Ansari Kwok-Wing Chau Zaher Mundher Yaseen Performance evaluation of sediment ejector efficiency using hybrid neuro-fuzzy models Engineering Applications of Computational Fluid Mechanics sediment ejector adaptive neuro-fuzzy inference systems hybrid model sediment removal efficiency metaheuristic models |
| title | Performance evaluation of sediment ejector efficiency using hybrid neuro-fuzzy models |
| title_full | Performance evaluation of sediment ejector efficiency using hybrid neuro-fuzzy models |
| title_fullStr | Performance evaluation of sediment ejector efficiency using hybrid neuro-fuzzy models |
| title_full_unstemmed | Performance evaluation of sediment ejector efficiency using hybrid neuro-fuzzy models |
| title_short | Performance evaluation of sediment ejector efficiency using hybrid neuro-fuzzy models |
| title_sort | performance evaluation of sediment ejector efficiency using hybrid neuro fuzzy models |
| topic | sediment ejector adaptive neuro-fuzzy inference systems hybrid model sediment removal efficiency metaheuristic models |
| url | http://dx.doi.org/10.1080/19942060.2021.1893224 |
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