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) met...
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MDPI AG
2021-03-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/9/6/596 |
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| author | Babak Lashkar-Ara Niloofar Kalantari Zohreh Sheikh Khozani Amir Mosavi |
| author_facet | Babak Lashkar-Ara Niloofar Kalantari Zohreh Sheikh Khozani Amir Mosavi |
| author_sort | Babak Lashkar-Ara |
| collection | DOAJ |
| description | 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 <i>B</i>/<i>H</i>, Where the transverse coordinate is <i>B</i>, and the flow depth is <i>H</i>. With the parameters (<i>b</i>/<i>B</i>), (<i>B</i>/<i>H</i>) for the bed and (<i>z</i>/<i>H</i>), (<i>B</i>/<i>H</i>) 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. |
| first_indexed | 2024-03-10T13:20:57Z |
| format | Article |
| id | doaj.art-11589c6801af4c58a6df6c6aae6ff81f |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-10T13:20:57Z |
| publishDate | 2021-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-11589c6801af4c58a6df6c6aae6ff81f2023-11-21T10:01:10ZengMDPI AGMathematics2227-73902021-03-019659610.3390/math9060596Assessing Machine Learning versus a Mathematical Model to Estimate the Transverse Shear Stress Distribution in a Rectangular ChannelBabak Lashkar-Ara0Niloofar Kalantari1Zohreh Sheikh Khozani2Amir Mosavi3Department of Civil Engineering, Jundi-Shapur University of Technology, Dezful 64616-18674, IranDepartment of Civil Engineering, Jundi-Shapur University of Technology, Dezful 64616-18674, IranInstitute of Structural Mechanics, Bauhaus Universität-Weimar, 99423 Weimar, GermanyInstitute of Structural Mechanics, Bauhaus Universität-Weimar, 99423 Weimar, GermanyOne 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 <i>B</i>/<i>H</i>, Where the transverse coordinate is <i>B</i>, and the flow depth is <i>H</i>. With the parameters (<i>b</i>/<i>B</i>), (<i>B</i>/<i>H</i>) for the bed and (<i>z</i>/<i>H</i>), (<i>B</i>/<i>H</i>) 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.https://www.mdpi.com/2227-7390/9/6/596smooth rectangular channelTsallis entropygenetic programmingartificial intelligencemachine learningbig data |
| spellingShingle | Babak Lashkar-Ara Niloofar Kalantari Zohreh Sheikh Khozani Amir Mosavi Assessing Machine Learning versus a Mathematical Model to Estimate the Transverse Shear Stress Distribution in a Rectangular Channel Mathematics smooth rectangular channel Tsallis entropy genetic programming artificial intelligence machine learning big data |
| title | Assessing Machine Learning versus a Mathematical Model to Estimate the Transverse Shear Stress Distribution in a Rectangular Channel |
| title_full | Assessing Machine Learning versus a Mathematical Model to Estimate the Transverse Shear Stress Distribution in a Rectangular Channel |
| title_fullStr | Assessing Machine Learning versus a Mathematical Model to Estimate the Transverse Shear Stress Distribution in a Rectangular Channel |
| title_full_unstemmed | Assessing Machine Learning versus a Mathematical Model to Estimate the Transverse Shear Stress Distribution in a Rectangular Channel |
| title_short | Assessing Machine Learning versus a Mathematical Model to Estimate the Transverse Shear Stress Distribution in a Rectangular Channel |
| title_sort | assessing machine learning versus a mathematical model to estimate the transverse shear stress distribution in a rectangular channel |
| topic | smooth rectangular channel Tsallis entropy genetic programming artificial intelligence machine learning big data |
| url | https://www.mdpi.com/2227-7390/9/6/596 |
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