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

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@Article{lashkar-ara:2021:Mathematics,

• author = "Babak Lashkar-Ara and Niloofar Kalantari and Zohreh {Sheikh Khozani} and Amir Mosavi",
• title = "Assessing Machine Learning versus a Mathematical Model to Estimate the Transverse Shear Stress Distribution in a Rectangular Channel",
• journal = "Mathematics",
• year = "2021",
• volume = "9",
• number = "6",
• keywords = "genetic algorithms, genetic programming",
• ISSN = "2227-7390",
• URL = "https://www.mdpi.com/2227-7390/9/6/596",
• DOI = "doi:10.3390/math9060596",
• 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 optimised 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 modelling 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.",
• notes = "also known as \cite{math9060596}",

}

Genetic Programming entries for Babak Lashkar-Ara Niloofar Kalantari Zohreh Sheikh Khozani Amir Mosavi

Citations