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:: Volume 8, Issue 29 (9-2021) ::
2021, 8(29): 40-51 Back to browse issues page
Estimation of Scour Depth around Submerged Weirs Using the Novel Approach Extreme Learning Machine
Ali Azizpor , Mohammad ali Izadbakhsh , Ahmad Rajabi , Saeid Shabanlou
Department of Water Engineering, College of Agriculture, Islamic Azad University, Kermanshah Branch, Kermanshah , ali.azizpour1978@gmail.com
Abstract:   (811 Views)
Scour in vicinity of hydraulic structures is considered as one of the most important parameters to design the structures. In this study, scour pattern at downstream of submerged weirs was predicted using the novel method “Extreme Learning Machine”. In current study, in order to survey the accuracy of the numerical model, the Monte Carlo Simulations(MCs) was employed. In addition, the k-fold cross validation was utilized so as to validate the results of numerical models. Then, regarding with input parameters, five ELM models were developed. Firstly, the number of optimized hidden neurons for soft computing model was calculated. Next, the best activation function for numerical model was chosen. Analysis of activation function showed that the sigmoid activation function predicted the scour around submerged weirs with more accuracy. Additionally, results of sensitivity analysis proved that the superior model estimated the scour in terms of U0/Uc, z/ht,d50/ht. This model simulated the scour around submerged weirs with reasonable accuracy, for instance, determination coefficient and scatter index were computed 0.880 and 0.127, respectively. Moreover, the velocity parameter U0/Uc was identified as the most effective input variable. Finally, a matrix was provided to estimate the scour depth for engineers without previous knowledge of Extreme Learning Machine.
 
Keywords: Submerged weir, Scour, Modeling, Extreme Learning Machine
Full-Text [PDF 1552 kb]   (379 Downloads)    
Type of Study: Research | Subject: سد و سازه
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azizpor A, izadbakhsh M A, rajabi A, shabanlou S. Estimation of Scour Depth around Submerged Weirs Using the Novel Approach Extreme Learning Machine. Iranian Dam and Hydroelectric Powerplant 2021; 8 (29) :40-51
URL: http://journal.hydropower.org.ir/article-1-361-en.html


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Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Volume 8, Issue 29 (9-2021) Back to browse issues page
نشریه سد و نیروگاه برق آبی Journal of Dam and Hydroelectric Powerplant
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