Using soft computing and machine learning algorithms to predict the discharge coefficient of curved labyrinth overflows

Using soft computing and machine learning algorithms to predict the discharge coefficient of curved labyrinth overflows

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 ma...

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Bibliographic Details
Main Authors: Zhenlong Hu, Hojat Karami, Alireza Rezaei, Yashar DadrasAjirlou, Md. Jalil Piran, Shahab S. Band, Kwok-Wing Chau, Amir Mosavi
Format: Article
Language:English
Published: Taylor & Francis Group 2021-01-01
Series:Engineering Applications of Computational Fluid Mechanics
Subjects:
discharge coefficient
labyrinth overflow
artificial intelligence
support vector machine (svm)
machine learning
Online Access:http://dx.doi.org/10.1080/19942060.2021.1934546

Internet

http://dx.doi.org/10.1080/19942060.2021.1934546

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