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

TL;DR

This study compares machine learning models like GP and ANFIS with Tsallis entropy equations for estimating shear stress distribution in rectangular channels, finding ML models more effective based on laboratory data.

Contribution

It introduces the application of GP and ANFIS models for shear stress estimation in rectangular channels and compares their performance with Tsallis entropy-based methods.

Findings

GP outperformed other models in accuracy.

ML models are more effective than entropy-based equations.

B/H ratio is the most influential parameter.

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 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.