A hydraulic jump is an abrupt transition between subcritical and supercritical flows which is associated with energy dissipation, air entrainment, spray, splashing, and surface waves. Both physical and numerical modeling were largely applied to study hydrodynamics, turbulence and air-entrainment in the hydraulic jump, while the literature about the application of classifier models is quite limited. Determining air-flow parameters and turbulent intensity has been merely performed by costly and time-consuming experimental methods, while this study is the first attempt to estimate the mentioned parameters using a computer-based methodology with desired precision. In the present study, air-flow parameters including void fraction (C) and bubble count rate (F), as well as turbulent intensity (Tu) on rough bed were estimated using Bayesian model averaging (BMA) and three multilayer perceptron (MLP), support vector regression (SVR) and generalized regression neural network (GRNN) as classifier models. To develop the stated models, the experimental data from Felder and Chanson (2016) were divided into four classes based on longitudinal distance from the jump toe. Results highlighted that the MLP and GRNN models have more accurate results compared to the SVR model. For F and Tu, the GRNN model and for C, the MLP model showed better performance than other models in four classes. The average acceptance rate between 15 and 30% of the BMA model performance for all classes proved the accuracy and efficiency of the proposed methodology. The average RMSE value of BMA results and the bests classifier models were 0.41 and 0.42, respectively, for the estimation of all three parameters. Results revealed that the BMA model by weighting individual classifier models could be able to estimate parameters with better accuracy than the best classifier model in each class. The significant outcome of this study is that the proposed model is able to render accurate results in a complex system such as hydraulic jump.

水力跃变是亚临界流和超临界流之间的突然过渡，与能量消散，空气夹带，喷雾，飞溅和表面波有关。物理模型和数值模型都广泛用于研究水力跃迁中的流体动力学，湍流和夹带空气，而关于分类器模型的应用文献却十分有限。确定气流参数和湍流强度仅是通过昂贵且耗时的实验方法完成的，而这项研究是首次尝试使用具有所需精度的基于计算机的方法来估算所述参数。在本研究中，气流参数包括空隙率（C）和气泡计数率（F），以及使用贝叶斯模型平均（BMA）和三个多层感知器（MLP），支持向量回归（SVR）和广义回归神经网络（GRNN）作为分类器模型来估算粗糙床上的湍流强度（Tu）。为了开发陈述的模型，根据与跳趾的纵向距离，将Felder和Chanson（2016）的实验数据分为四类。结果强调，与SVR模型相比，MLP和GRNN模型具有更准确的结果。对于F和Tu，GRNN模型和C，MLP模型在四个类别中显示出比其他模型更好的性能。所有类别的BMA模型性能的平均接受率在15％到30％之间，证明了所提出方法的准确性和效率。对于所有三个参数的估计，BMA结果和最佳分类器模型的平均RMSE值分别为0.41和0.42。结果表明，通过加权各个分类器模型，BMA模型可以比每个分类中的最佳分类器模型更好地估计参数。这项研究的重要成果是，所提出的模型能够在复杂的系统（例如液压跳跃）中提供准确的结果。