Due to the vital role of rivers and canals, the protection of their banks and beds is critically important. There are various methods for protecting beds and banks of rivers and canals in which “cross-vane structures” is one of them. In this paper, the scour hole depth at the downstream of cross-vane structures with different shapes (i.e., J, I, U, and W) is simulated utilizing a modern artificial intelligence method entitled “Outlier Robust Extreme Learning Machine (ORELM)”. The observational data are divided into two groups: training (70%) and test (30%). After that, the most optimal activation function for simulating the scour depth at the downstream of cross-vane structures is selected. Then, using the input parameters including the ratio of the structure length to the channel width (b/B), the densimetric Froude number (F_d), the ratio of the difference between the downstream and upstream depths to the structure height (Δy/h_st) and the structure shape factor \( \left( \phi \right) \), eleven different ORELM models are developed for estimating the scour depth. Subsequently, the suitable model and also the most effective input parameters are identified through the conduction of an uncertainty analysis. The suitable model simulates the scour values by the dimensionless parameters b/B, F_d, Δy/h_st. For this model, the values of the correlation coefficient (R), Variance accounted for (VAF) and the Nash-Sutcliffe efficiency (NSC) for the suitable model in the test mode are obtained 0.956, 91.378 and 0.908, respectively. Also, the dimensionless parameters b/B, Δy/h_st. are detected as the most effective input parameters. Furthermore, the results of the suitable model are compared with the extreme learning machine model and it is concluded that the ORELM model is more accurate. Moreover, an uncertainty analysis exhibits that the ORELM model has an overestimated performance. Besides, a partial derivative sensitivity analysis (PDSA) model is performed for the suitable model.

由于河流和运河的重要作用，保护其河岸和河床至关重要。保护河床和河床的河床和河岸有多种方法，其中“跨叶片结构”就是其中之一。在本文中，采用名为“离群鲁棒极限学习机（ORELM）”的现代人工智能方法模拟了具有不同形状（即J，I，U和W）的交叉叶片结构下游的冲孔深度。 。观察数据分为两组：训练（70％）和测试（30％）。之后，选择用于模拟横叶片结构下游冲刷深度的最佳最优激活函数。然后，使用包括结构长度与通道宽度之比（b / B），密度弗洛德数（F _d），下游和上游深度之间的差与结构高度的比（Δy / h _st）和结构形状因子\（\ left（\ phi \ right）\），开发了11种不同的ORELM模型来估算冲刷深度。随后，通过进行不确定性分析，确定合适的模型以及最有效的输入参数。合适的模型由无量纲参数模拟冲刷值b / B，F _d，ΔY / ħ _ST。对于此模型，在测试模式下，适用模型的相关系数（R），占方差（VAF）和纳什-苏克利夫效率（NSC）的值分别为0.956、91.378和0.908。同样，无量纲参数b / B，Δy / h _st。被检测为最有效的输入参数。此外，将合适模型的结果与极限学习机模型进行了比较，得出的结论是ORELM模型更准确。此外，不确定性分析表明ORELM模型的性能被高估了。此外，针对适当的模型执行偏导数敏感性分析（PDSA）模型。