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

  • 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 theOne 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.show moreshow less

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Metadaten
Document Type:Article
Author: Babak Lashkar-AraORCiD, Niloofar Kalantari, Zohreh Sheikh KhozaniORCiD, Amir MosaviORCiD
DOI (Cite-Link):https://doi.org/10.3390/math9060596Cite-Link
URN (Cite-Link):https://nbn-resolving.org/urn:nbn:de:gbv:wim2-20210504-44197Cite-Link
URL:https://www.mdpi.com/2227-7390/9/6/596
Parent Title (German):Mathematics
Publisher:MDPI
Place of publication:Basel
Language:English
Date of Publication (online):2021/03/11
Date of first Publication:2021/03/11
Release Date:2021/05/04
Publishing Institution:Bauhaus-Universität Weimar
Institutes and partner institutions:Fakultät Bauingenieurwesen / Institut für Strukturmechanik
Volume:2021
Issue:Volume 9, Issue 6, Article 596
Pagenumber:15
Tag:Tsallis entropy; artificial intelligence; big data; computational hydraulics; genetic programming; machine learning; smooth rectangular channel
GND Keyword:Maschinelles Lernen
Dewey Decimal Classification:600 Technik, Medizin, angewandte Wissenschaften
BKL-Classification:56 Bauwesen
Licence (German):License Logo Creative Commons 4.0 - Namensnennung (CC BY 4.0)