Accurate determination of the discharge coefficient play a very important role in estimating the flow discharge over the weirs. As a result, it is significant to estimate the discharge coefficient correctly. The aim of this study is simulation and estimation the discharge coefficients (C_d) over the broad-crested weirs with cross section rectangular and suppressed. Hence, numerical simulation of hydraulic characteristics of these weirs were performed by ANSYS FLUENT software and results were obtained. Then two intelligent models of ANN, GPR and hybrid both of models namely ANN-HHO, GPR-HHO were used to determine the discharge coefficients using the efficient parameters and the results of these models were compared. Assessment of the results were performed using the statistical metrics: coefficient of determination (R²), root mean square error (RMSE), mean absolute error (MAE), scatter index (SI) and Kling-Gupta efficiency (KGE) and graphical diagrams including violin plot, percent relative error (RE%) plot and probability density function (PDF) plot of residuals. It was found that hybrid artificial neural network and gaussian process regression with Harris Hawks optimization (ANN-HHO and GPR-HHO) could improve ANN and GPR models performance in estimating the C_d in broad-crested weirs. Overall, results indicated that a combination of the HHO with the ANN (ANN-HHO) model performs better than GPR-HHO model for the estimation of the C_d.

准确确定流量系数在估算堰上的流量流量中起着非常重要的作用。结果，正确估计放电系数很重要。这项研究的目的是模拟和估计横截面为矩形且被抑制的宽顶堰上的排放系数（C _d）。因此，利用ANSYS FLUENT软件对这些堰的水力特性进行了数值模拟，并获得了结果。然后分别建立了ANN，GPR和混合两种智能模型，即ANN-HHO，GPR-HHO用有效参数确定排放系数，并比较了这些模型的结果。使用以下统计指标对结果进行评估：确定系数（R ²），均方根误差（RMSE），平均绝对误差（MAE），分散指数（SI）和克林古普塔效率（KGE）和图表包括小提琴图，相对误差百分比（RE％）图和残差的概率密度函数（PDF）图。研究发现，混合人工神经网络和高斯过程回归与哈里斯·霍克斯（Harris Hawks）优化（ANN-HHO和在估算宽顶堰中的C _d时，GPR-HHO可以提高ANN和GPR模型的性能。总体而言，结果表明，HHO与ANN（ANN-HHO）模型的组合在评估C _d方面比GPR-HHO模型更好。