Assessing Machine Learning versus a Mathematical Model to Estimate the Transverse Shear Stress Distribution in a Rectangular Channel

Abstract

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 for were used. The results of the sensitivity analysis show that the most influential parameter for the SSD in 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.

Global Sensitivity Analysis Based on Entropy: From Differential Entropy to Alternative Measures

“…The cross-sectional area is expressed as the product of the thickness and width. The resistance R is the product of yield strength f y and cross-sectional area t 2 •b-see Equation (20).…”

“…Another group of tasks uses entropy to examine the state of a system in combination with certain types of SA, which may not be based on entropy. This group includes, for example, SA of the working process of heat exchangers [18], the hydraulic reliability of water distribution systems [19], shear stress distribution in a rectangular channel [20], creep of soft marine soil [21], air energy storage systems in coal-fired power plants [22], uncertainties of mathematical decision-making models [23][24][25][26][27], and many others.…”