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|Title:||Performance evaluation of sediment ejector efficiency using hybrid neuro-fuzzy models||Authors:||Sharafati, A
|Issue Date:||2021||Source:||Engineering applications of computational fluid mechanics, 2021, v. 15, no. 1, p. 627-643||Abstract:||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.||Keywords:||Adaptive neuro-fuzzy inference systems
Sediment removal efficiency
|Publisher:||Hong Kong Polytechnic University, Department of Civil and Structural Engineering||Journal:||Engineering applications of computational fluid mechanics||ISSN:||1994-2060||EISSN:||1997-003X||DOI:||10.1080/19942060.2021.1893224||Rights:||© 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
The following publication Sharafati, A., Haghbin, M., Tiwari, N. K., Bhagat, S. K., Al-Ansari, N., Chau, K. W., & Yaseen, Z. M. (2021). Performance evaluation of sediment ejector efficiency using hybrid neuro-fuzzy models. Engineering Applications of Computational Fluid Mechanics, 15(1), 627-643 is available at https://doi.org/10.1080/19942060.2021.1893224
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