Despite modern advances used to estimate the discharge coefficient (), it is still a major challenge for hydraulic engineers to accurately determine for side weirs. In this study, extra tree regression (ETR) was used to predict the of rectangular sharp-crested side weirs depending on hydraulic and geometrical parameters. The prediction capacity of the ETR model was validated with two predictive models, namely, extreme learning machine (ELM) and random forest (RF). The quantitative assessment revealed that the ETR model achieved the highest accuracy in the predictions compared to other applied models, and also, it exhibited excellent agreement between measured and predicted (correlation coefficient is 0.9603). Moreover, the ETR achieved 6.73% and 22.96% higher prediction accuracy in terms of root mean square error in comparison to ELM and RF, respectively. Furthermore, the performed sensitivity analysis shows that the geometrical parameter such as b/B has the most influence on . Overall, the proposed model (ETR) is found to be a suitable, practical, and qualified computer-aid technology for modeling that may contribute to enhance the basic knowledge of hydraulic considerations.

中文翻译：

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用于流量系数预测的额外树回归模型：在水利领域的新颖实用应用和未来研究方向

尽管用于估计流量系数 ( ) 的现代技术进步，但准确确定侧堰仍然是液压工程师面临的主要挑战。在这项研究中，额外的树回归（ETR）用于根据水力和几何参数预测矩形尖顶侧堰。ETR模型的预测能力通过两种预测模型进行验证，即极限学习机（ELM）和随机森林（RF）。定量评估表明，与其他应用模型相比，ETR 模型在预测中实现了最高的准确度，并且在测量值和预测值之间表现出极好的一致性。（相关系数为 0.9603）。此外，与 ELM 和 RF 相比，ETR 在均方根误差方面的预测精度分别提高了 6.73% 和 22.96%。此外，执行的敏感性分析表明，几何参数如b / B对 的影响最大。总体而言，所提出的模型 (ETR) 被认为是一种合适、实用且合格的计算机辅助建模技术，可能有助于增强水力考虑的基本知识。