Abstract Accurate prediction of the long-term average dimensions of alluvial stable channels is a significant problem in river engineering. The goal of this research is to investigate the effect of flow discharge (Q), mean sediment size (d50) and Shields parameter (τ∗) on the stable channel dimensions by employing non-linear regression (NLR) and two Artificial Intelligence (AI) methods, including: Generalized Structure of Group Method of Data Handling (GS-GMDH) neural network and Gene Expression Programming (GEP). Discharge, grain size and Shields parameter from 85 gaging stations situated in three stable Iranian rivers were used as input data for the three methods to estimate the water-surface width (W), average flow depth (D) and longitudinal slope (S) of the rivers. Based on the results, it was found that the GS-GMDH produced more accurate results for simulating the channel width with a Mean Absolute Relative Error (MARE) value of 0.055; and GEP produced better estimations for channel depth and slope with MARE values of 0.035 and 0.03, respectively. Furthermore, by employing Artificial Intelligence (AI) methods (GS-GMDH and GEP), the RMSE values decreased by 22%, 25% and 75% in predicting width, depth, and slope, respectively, compared to NLR method. The overall results showed that the AI methods generally produced better estimations than the non-linear regression method. To determine the effect of each input variable (Q, d50, τ∗) on the target variables (W, D, S), a sensitivity analysis comprising various combinations of input variables was conducted. Based on the results, the flow discharge had a dominant role on depth and width estimation of stable channels. In slope estimation, the most important parameter was τ∗ and then d50, while the discharge had a weak effect on slope prediction. In general, the Shields parameter was the most effective parameter in depth and specially slope estimation of stable channels.

中文翻译：

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使用进化神经网络设计稳定的冲积通道

摘要 准确预测冲积稳定河道的长期平均尺寸是河流工程中的一个重要问题。本研究的目的是通过采用非线性回归 (NLR) 和两个人工智能 (AI) 来研究流量 (Q)、平均沉积物尺寸 (d50) 和盾构参数 (τ∗) 对稳定通道尺寸的影响) 方法，包括：数据处理组方法的广义结构 (GS-GMDH) 神经网络和基因表达式编程 (GEP)。来自位于伊朗三条稳定河流的 85 个测量站的流量、粒度和盾构参数被用作三种方法的输入数据，以估计水面宽度 (W)、平均水深 (D) 和纵向坡度 (S)河流。根据结果​​，发现 GS-GMDH 为模拟通道宽度产生了更准确的结果，平均绝对相对误差 (MARE) 值为 0.055；和 GEP 对通道深度和坡度产生了更好的估计，MARE 值分别为 0.035 和 0.03。此外，通过采用人工智能 (AI) 方法（GS-GMDH 和 GEP），与 NLR 方法相比，RMSE 值在预测宽度、深度和坡度方面分别降低了 22%、25% 和 75%。总体结果表明，人工智能方法通常比非线性回归方法产生更好的估计。为了确定每个输入变量（Q、d50、τ*）对目标变量（W、D、S）的影响，进行了包括输入变量的各种组合的敏感性分析。根据结果​​，流量对稳定通道的深度和宽度估计起主导作用。在坡度估计中，最重要的参数是 τ∗，然后是 d50，而流量对坡度预测的影响很小。一般来说，Shields 参数是深度和稳定通道斜率估计中最有效的参数。