This study compares several advanced machine learning models to obtain the most accurate method for predicting the aeration efficiency (E₂₀) through the Parshall flume. The required dataset is obtained from the laboratory tests using different flumes fabricated in National Institute Technology Kurukshetra, India. Besides, the potential of K Nearest Neighbor (KNN), Random Forest Regression (RFR), and Decision Tree Regression (DTR) models are evaluated to predict the aeration efficiency. In this way, several input combinations (e.g. M1-M15) are provided using the laboratory parameters (e.g. W/L, S/L, Fr, and Re). Different predictive models are obtained based on those input combinations and machine learning models proposed in the present study. The predictive models are assessed based on several performance metrics and visual indicators. Results show that the KNN-M11 model ($RMS{E}_{testing}=0.002,R_{testing}^2=0.929$), which includes W/L, S/L, and Fr as predictive variables outperforms the other predictive models. Furthermore, an enhancement is observed in KNN model estimation accuracy compared to the previously developed empirical models. In general, the predictive model dominated in the present study provides adequate performance in predicting the aeration efficiency in the Parshall flume.

本研究比较了几种先进的机器学习模型以获得最准确的预测充气效率的方法（E ₂₀）穿过Parshall的水槽。所需的数据集是使用在印度国立技术大学Kurukshetra制造的不同水槽从实验室测试中获得的。此外，还评估了K最近邻（KNN），随机森林回归（RFR）和决策树回归（DTR）模型的潜力，以预测曝气效率。这样，使用实验室参数（例如W / L，S / L，Fr和Re）提供了几种输入组合（例如M1-M15）。基于这些输入组合和本研究中提出的机器学习模型，可以获得不同的预测模型。基于几个性能指标和视觉指标对预测模型进行评估。结果表明，KNN-M11模型（$\mathrm{[R}\mathrm{中号}\mathrm{小号}{E}_{ŤËsŤ\mathrm{一世}ñG}=0.002，{\mathrm{[R}}_{ŤËsŤ\mathrm{一世}ñG}^{\mathrm{2个}}=0.929$），其中W / L，S / L和Fr作为预测变量优于其他预测模型。此外，与先前开发的经验模型相比，KNN模型估计的准确性得到了提高。通常，在本研究中占主导地位的预测模型在预测Parshall槽中的通气效率方面提供了足够的性能。