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Title: Using soft computing and machine learning algorithms to predict the discharge coefficient of curved labyrinth overflows
Authors: Hu, Z
Karami, H
Rezaei, A
DadrasAjirlou, Y
Piran, MJ
Band, SS
Chau, KW 
Mosavi, A
Issue Date: 2021
Source: Engineering applications of computational fluid mechanics, 2021, v. 15, no. 1, p. 1002-1015
Abstract: This research aims to estimate the overflow capacity of a curved labyrinth using different intelligent prediction models, namely the adaptive neural-fuzzy inference system, the support vector machine, the M5 model tree, the least-squares support vector machine and the least-squares support vector machine–bat algorithm (LSSVM-BA). A total of 355 empirical data for 6 different congressional overflow models were extracted from the results of a laboratory study on labyrinth overflow models. The parameters of the upstream water head to overflow ratio, the lateral wall angle and the curvature angle were used to estimate the discharge coefficient of curved labyrinth overflows. Based on various statistical evaluation indicators, the results show that those input parameters can be relied upon to predict the discharge coefficient. Specifically, the LSSVM-BA model showed the best prediction accuracy during the training and test phases. Such a low-cost prediction model may have a remarkable practical implication as it could be an economic alternative to the expensive laboratory solution, which is costly and time-consuming.
Keywords: Artificial intelligence
Discharge coefficient
Labyrinth overflow
Machine learning
Support vector machine (SVM)
Publisher: Hong Kong Polytechnic University, Department of Civil and Structural Engineering
Journal: Engineering applications of computational fluid mechanics 
ISSN: 1994-2060
EISSN: 1997-003X
DOI: 10.1080/19942060.2021.1934546
Rights: © 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis GroupThis is an Open Access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
The following publication Hu, Z., Karami, H., Rezaei, A., DadrasAjirlou, Y., Piran, M. J., Band, S. S., ... & Mosavi, A. (2021). Using soft computing and machine learning algorithms to predict the discharge coefficient of curved labyrinth overflows. Engineering Applications of Computational Fluid Mechanics, 15(1), 1002-1015 is available at
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