论文标题
使用数据挖掘和机器学习模型在对称化合物通道中的剪切应力分布预测
Shear Stress Distribution Prediction in Symmetric Compound Channels Using Data Mining and Machine Learning Models
论文作者
论文摘要
开放通道中的剪切应力分布预测在液压结构工程中至关重要，因为它直接影响稳定通道的设计。在这项研究中，首先进行了一系列实验测试，以评估棱柱形化合物通道中的剪切应力分布。在化合物通道中测量了整个湿周周周围的剪切应力值，在亚临界和超临界条件下，在不同的流动深度也不同的洪泛区宽度。一组数据挖掘和机器学习模型，包括随机森林（RF），M5P，随机委员会（RC），KSTAR和加性回归模型（AR），以预测化合物通道中的剪切应力分布。结果在这五个模型中指示，RF方法表明最精确的结果，最高R2值为0.9。最后，在这项研究（RF）中研究的最强大的数据挖掘方法与两个著名的Shiono和Knight方法（SKM）的分析模型（SKM）和Shannon方法相比，以获取在预测剪切应力分布方面功能的拟议模型。结果表明，与SKM和香农模型相比，RF模型具有最佳的预测性能。
Shear stress distribution prediction in open channels is of utmost importance in hydraulic structural engineering as it directly affects the design of stable channels. In this study, at first, a series of experimental tests were conducted to assess the shear stress distribution in prismatic compound channels. The shear stress values around the whole wetted perimeter were measured in the compound channel with different floodplain widths also in different flow depths in subcritical and supercritical conditions. A set of, data mining and machine learning models including Random Forest (RF), M5P, Random Committee (RC), KStar and Additive Regression Model (AR) implemented on attained data to predict the shear stress distribution in the compound channel. Results indicated among these five models, RF method indicated the most precise results with the highest R2 value of 0.9. Finally, the most powerful data mining method which studied in this research (RF) compared with two well-known analytical models of Shiono and Knight Method (SKM) and Shannon method to acquire the proposed model functioning in predicting the shear stress distribution. The results showed that the RF model has the best prediction performance compared to SKM and Shannon models.
