TY - JOUR
T1 - Performance evaluation of sediment ejector efficiency using hybrid neuro-fuzzy models
AU - Sharafati, Ahmad
AU - Masoud, Haghbin
AU - Tiwari, Nand Kumar
AU - Bhagat, Suraj Kumar
AU - Al-Ansari, Nadhir
AU - Chau, Kwok Wing
AU - Yaseen, Zaher Mundher
N1 - Publisher Copyright:
© 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
PY - 2021/4
Y1 - 2021/4
N2 - 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.
AB - 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.
KW - adaptive neuro-fuzzy inference systems
KW - hybrid model
KW - metaheuristic models
KW - Sediment ejector
KW - sediment removal efficiency
UR - http://www.scopus.com/inward/record.url?scp=85103859910&partnerID=8YFLogxK
U2 - 10.1080/19942060.2021.1893224
DO - 10.1080/19942060.2021.1893224
M3 - Journal article
AN - SCOPUS:85103859910
SN - 1994-2060
VL - 15
SP - 627
EP - 643
JO - Engineering Applications of Computational Fluid Mechanics
JF - Engineering Applications of Computational Fluid Mechanics
IS - 1
ER -