Performance evaluation of sediment ejector efficiency using hybrid neuro-fuzzy models

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.