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

Babak Lashkar-Ara, Niloofar Kalantari, Zohreh Sheikh Khozani and Amir Mosavi
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Babak Lashkar-Ara: Department of Civil Engineering, Jundi-Shapur University of Technology, Dezful 64616-18674, Iran
Niloofar Kalantari: Department of Civil Engineering, Jundi-Shapur University of Technology, Dezful 64616-18674, Iran
Zohreh Sheikh Khozani: Institute of Structural Mechanics, Bauhaus Universität-Weimar, 99423 Weimar, Germany
Amir Mosavi: Institute of Structural Mechanics, Bauhaus Universität-Weimar, 99423 Weimar, Germany

Mathematics, 2021, vol. 9, issue 6, 1-15

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.

Keywords: smooth rectangular channel; Tsallis entropy; genetic programming; artificial intelligence; machine learning; big data; computational hydraulics; neuro-fuzzy; data science (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
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