The advancement in computer aid and artificial intelligence (AI) models have received a noticeable progression in several engineering applications. In this research, an investigation for the capacity of a hybrid artificial intelligence model for predicting depth scouring of submerged weir. Scouring phenomena is one of the most complex problems in the field of the river and hydraulic engineering. Accurate and precise prediction for the depth scouring ($d_s$) is one of the essential processes for maintaining a sustainable hydraulic structure. This article introduces a new predictive model called tBPSO-SVR, which is a hybridization of an enhanced binary particle swarm optimization (PSO) algorithm with support vector regression (SVR) model as an efficient predictive model. The roles of the PSO algorithm are tuning the internal hyperparameters of the SVR model in addition to the optimization of the predictors selection “feature selection” for the $d_s$ modeling. The prediction matrix is constructed based on several related geometric dimensions, flow information and sediment properties. The proposed model is validated against several well-established machine learning models introduced over the literature. The prediction potential of the proposed tBPSO-SVR model exhibited a superior capability. In quantitative terms, tBPSO-SVR attained minimum mean absolute error (MAE = 0.012 m) and maximum coefficient of determination ($R^2$ = 0.956). Remarkably, the proposed hybrid artificial intelligence demonstrated an efficient prediction model for depth scouring prediction with reducing the input parameters.

计算机辅助和人工智能（AI）模型的进步已经在几种工程应用中得到了明显的发展。在这项研究中，研究了一种混合人工智能模型预测淹没堰深度冲刷的能力。冲刷现象是河流和水利工程领域中最复杂的问题之一。准确，准确地预测深度$d_s$）是维持可持续水力结构的重要过程之一。本文介绍了一种称为tBPSO-SVR的新预测模型，该模型是增强型二进制粒子群优化（PSO）算法与作为有效预测模型的支持向量回归（SVR）模型的混合。PSO算法的作用是优化SVR模型的内部超参数，此外还优化了预测变量选择“功能选择”。$d_s$造型。预测矩阵是基于几个相关的几何尺寸，流量信息和沉积物属性构造的。相对于文献中介绍的几种公认的机器学习模型，对提出的模型进行了验证。提出的tBPSO-SVR模型的预测潜力表现出了卓越的能力。从数量上讲，tBPSO-SVR达到了最小平均绝对误差（MAE = 0.012 m）和最大测定系数（${\mathrm{[R}}^2$= 0.956）。值得注意的是，提出的混合人工智能演示了一种有效的预测模型，用于减少输入参数的深度精练预测。