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 CC_Test = 0.916.

摘要

喷射器中的泥沙输送是高度随机且非线性的，其准确估算是一项复杂而具有挑战性的任务。本研究试图使用新开发的混合数据智能模型研究泥沙喷射器的泥沙去除估计。所提出的模型基于自适应神经模糊推理系统（ANFIS）与不同的元启发式算法（即粒子群优化（PSO），遗传算法（GA），差分进化（DE）和蚁群优化（ACO））的混合）。所提出的模型是由各种相关的输入变量构成的，例如泥沙浓度，流量深度，速度，泥沙大小，弗洛德数，提取比，隧道和子隧道的数量以及在泥沙喷射器上游的流量深度。已开发的混合模型的估计能力使用几个统计评估指标进行评估。为研究的喷射器沉积物去除估算而获得的建模结果显示出了乐观的发现。在已开发的混合模型中，ANFIS-PSO模型表现出最佳的可预测性，相关系数值为CC_列车 = 0.915，CC_测试 = 0.916。