Sedimentation in open channels occurs frequently and is relative to system inflow. The long-term retention of sediments on channel beds can increase the possibility of variations in deposits and their eventual consolidation. This study compares three hybrid artificial intelligence methods in estimating sediment transport without sedimentation (STWS). We employed the Particle Swarm Optimization (PSO), Imperialist Competitive Algorithm (ICA) and Genetic Algorithm (GA) methods in combination with the Artificial Neural Network (ANN) to overcome the weakness of ANN training with conventional algorithms. We used the ICA, GA and PSO methods to optimize the weights of the ANN layers. Using dimensional analysis, we placed the effective parameters in predicting sediment transport into five non-dimensional groups. Six models are proposed and run using three hybrid methods (18 models in total). As the comparisons demonstrate, the proposed combined models are more accurate than ANN and existing equations in estimating the densimetric Froude number (Fr). However, we found the ICA–ANN superior to GA–ANN and PSO–ANN, as it produces explicit solutions to the problem. The ICA–ANN has the lowest prediction uncertainty band for Fr of all developed models. Moreover, the variation trend of the Fr for all input variables (except overall friction factor of sediment) is a second-order polynomial.

明渠中的沉积物经常发生，并且与系统流入有关。沉积物在河床床上的长期滞留会增加沉积物变化及其最终固结的可能性。这项研究比较了三种混合人工智能方法来估计无沉淀物的泥沙运移（STWS）。我们将粒子群优化（PSO），帝国主义竞争算法（ICA）和遗传算法（GA）与人工神经网络（ANN）结合使用，以克服常规算法对ANN训练的弱点。我们使用ICA，GA和PSO方法来优化ANN层的权重。使用尺寸分析，我们将预测泥沙运移的有效参数分为五个非尺寸组。提出了六个模型，并使用三种混合方法运行（总共18个模型）。如比较所示，在估计密度弗洛德数（Fr）时，所提出的组合模型比ANN和现有方程更为准确。但是，我们发现ICA–ANN优于GA–ANN和PSO–ANN，因为它为问题提供了明确的解决方案。在所有已开发模型中，ICA-ANN的Fr预测不确定性范围最低。而且，Fr对于所有输入变量（沉积物的总摩擦因数除外）的变化趋势都是二阶多项式。因为它为问题提供了明确的解决方案。在所有已开发模型中，ICA-ANN的Fr预测不确定性范围最低。而且，Fr对于所有输入变量（沉积物的总摩擦因数除外）的变化趋势都是二阶多项式。因为它为问题提供了明确的解决方案。在所有已开发模型中，ICA-ANN的Fr预测不确定性范围最低。而且，Fr对于所有输入变量（沉积物的总摩擦因数除外）的变化趋势都是二阶多项式。