Prognostication of scour around twin and three piers using efficient outlier robust extreme learning machine

Nou, Mohammad Rashki Ghaleh; Foroudi, Ali; Latif, Sarmad Dashti; Parsaie, Abbas

doi:10.1007/s11356-022-20681-5

Cited by 7 publications

(2 citation statements)

References 35 publications

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“…The GMDH structure is determined systematically, unlike the perceptron multilayer neural network model whose structure is mainly developed based on the designer's experience (Saberi-Movahed et al 2020). Currently, the GMDH model is successfully used in most hydraulic engineering problems (Yonesi et al 2022), such as predicting the scour around hydraulic structures (Nou et al 2022) and the discharge coefficient of weirs (Parsaie and Haghiabi 2020). The GMDH model was developed based on the extension of partial descriptions.…”

Section: Group Methods Of Data Handling (Gmdh)mentioning

confidence: 99%

Estimation of Manning roughness coefficient in alluvial rivers with bed forms using soft computing models

Yarahmadi

Parsaie

Shafai-Bejestan

et al. 2023

Preprint

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The bed surface of alluvial rivers is rarely plane and takes different geometric configurations called bed forms. Bed forms are created by the movement of riverbed sediments, especially during floods. The interaction between the flow and bed form is very complex. The flow intensity controls bed forms, and the bed form significantly affects the properties of the flow (such as depth, velocity, and flow resistance). The Manning roughness coefficient is one of the most important flow resistance coefficients, which significantly affects the bed form shape and geometry. This study aimed to estimate the Manning roughness coefficient in rivers with bed forms, using soft computing models, including multilayer perceptron artificial neural network (MLPNN), group method of data handling (GMDH), support vector machine (SVM) model, and genetic programming model (GP). To this end, the energy grade line (Sf), flow Froude number (Fr), y/d50, ∆/d50, ∆\λ, and ∆/y were used as the input variables, and the Manning roughness coefficient was used as the output variable. The results showed that all the test models have acceptable accuracy, while the SVM model showed the highest level of accuracy with the coefficient of determination R2=0.99 in the verification stage. The sensitivity analysis of SVM and MLPNN models and the structural analysis of GMDH and GP models indicated that the most important parameters affecting the Manning roughness coefficient are Fr, Sf, ∆\λ.

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Section: Group Methods Of Data Handling (Gmdh)mentioning

confidence: 99%

Estimation of Manning roughness coefficient in alluvial rivers with bed forms using soft computing models

Yarahmadi

Parsaie

Shafai-Bejestan

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

Section: Group Methods Of Data Handling (Gmdh)mentioning

confidence: 99%

Estimation of Manning Roughness Coefficient in Alluvial Rivers with Bed Forms Using Soft Computing Models

Yarahmadi

Parsaie

Shafai-Bejestan

et al. 2023

Water Resour Manage

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The bed surface of alluvial rivers is rarely plane and takes different geometric configurations called bed forms. Bed forms are created by the movement of riverbed sediments, especially during floods. The interaction between the flow and bed form is very complex. The flow intensity controls bed forms, and the bed form significantly affects the properties of the flow (such as depth, velocity, and flow resistance). The Manning roughness coefficient is one of the most important flow resistance coefficients, which significantly affects the bed form shape and geometry. This study aimed to estimate the Manning roughness coefficient in rivers with bed forms, using soft computing models, including multilayer perceptron artificial neural network 2 (MLPNN), group method of data handling (GMDH), support vector machine (SVM) model, and genetic programming model (GP). To this end, the energy grade line (𝑆 𝑓 ), flow Froude number (Fr), 𝑦 𝑑 50 ⁄ , ∆ 𝑑 50 ⁄ , ∆ 𝜆 ⁄ , and ∆ 𝑦 ⁄ were used as the input variables, and the Manning roughness coefficient was used as the output variable. The results showed that all the test models have acceptable accuracy, while the SVM model showed the highest level of accuracy with the coefficient of determination 𝑅 2 = 0.99 in the verification stage. The sensitivity analysis of SVM and MLPNN models and the structural analysis of GMDH and GP models indicated that the most important parameters affecting the Manning roughness coefficient areFr, 𝑆 𝑓 , ∆ 𝜆 ⁄ .

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Development of Soft Computing Models Based on Wavelet Analysis for Estimating Piezometric Heads in Earth Dams

Saeid,

Irandoust,

Jalalkamali

2023

Iran J Sci Technol Trans Civ Eng

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Prognostication of scour around twin and three piers using efficient outlier robust extreme learning machine

Cited by 7 publications

References 35 publications

Estimation of Manning roughness coefficient in alluvial rivers with bed forms using soft computing models

Estimation of Manning roughness coefficient in alluvial rivers with bed forms using soft computing models

Estimation of Manning Roughness Coefficient in Alluvial Rivers with Bed Forms Using Soft Computing Models

Development of Soft Computing Models Based on Wavelet Analysis for Estimating Piezometric Heads in Earth Dams

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