机器学习与数学模型在矩形通道横向剪应力分布估计中的比较
Machine Learning versus Mathematical Model to Estimate the Transverse Shear Stress Distribution in a Rectangular Channel
摘要：
矩形渠道中床面剪应力和壁面剪应力横向分布的可靠性估算是水利工程中的重要课题之一。本文利用Tsallis熵、遗传规划和自适应神经模糊推理系统等方法对矩形槽道中的剪应力分布进行了研究。为了评估Tsallis熵、GP和ANFIS模型的结果，使用了实验室观察，其中使用优化的Preston管测量剪切应力。然后，这用于测量矩形通道中各种纵横比的SSD。为了研究剪切应力百分比，使用了10个数据系列，共112个不同的数据。灵敏度分析结果表明，对光滑矩形槽道中的SSD影响最大的参数是无量纲参数B/H，其中横坐标为B，水深为H。以床层参数（b/B）、（B/H）和壁面参数（z/H）、（B/H）为输入，GP模型的建模效果较好。基于分析，可以得出结论，使用GP和ANFIS算法是更有效地估计在光滑矩形通道中的剪切应力比Tsallis熵为基础的方程。
英文摘要：
One of the most important subjects of hydraulic engineering is the reliable estimation of the transverse distribution in the rectangular channel of bed and wall shear stresses. This study makes use of the Tsallis entropy, genetic programming (GP) and adaptive neuro-fuzzy inference system (ANFIS) methods to assess the shear stress distribution (SSD) in the rectangular channel. To evaluate the results of the Tsallis entropy, GP and ANFIS models, laboratory observations were used in which shear stress was measured using an optimized Preston tube. This is then used to measure the SSD in various aspect ratios in the rectangular channel. To investigate the shear stress percentage, 10 data series with a total of 112 different data were used. The results of the sensitivity analysis show that the most influential parameter for the SSD in a smooth rectangular channel is the dimensionless parameter B/H, Where the transverse coordinate is B, and the flow depth is H. With the parameters (b/B), (B/H) for the bed and (z/H), (B/H) for the wall as inputs, the modeling of the GP was better than the other one. Based on the analysis, it can be concluded that the use of GP and ANFIS algorithms is more effective in estimating shear stress in smooth rectangular channels than the Tsallis entropy-based equations.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
