Predicting discharge coefficient of triangular labyrinth weir using extreme learning machine, artificial neural network and genetic programming

Weirs are a type of hydraulic structure used to direct and transfer water flows in the canals and overflows in the dams. The important index in computing flow discharge over the weir is discharge coefficient ( C d ). The aim of this study is accurate determination of the C d in triangular labyrinth...

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Veröffentlicht in:	Neural computing & applications Jg. 29; H. 11; S. 983 - 989
Hauptverfasser:	Karami, Hojat, Karimi, Sohrab, Bonakdari, Hossein, Shamshirband, Shahabodin
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London Springer London 01.06.2018 Springer Nature B.V
Schlagworte:	Artificial Intelligence Artificial neural networks Canals Computational Biology/Bioinformatics Computational Science and Engineering Computer Science Data Mining and Knowledge Discovery Discharge coefficient Genetic algorithms Image Processing and Computer Vision Learning theory Neural networks Predictions Probability and Statistics in Computer Science Review Root-mean-square errors Statistical methods Test procedures Training Weirs Artificial neural network Discharge coefficient Extreme learning machine Genetic programming Weir
ISSN:	0941-0643, 1433-3058
Online-Zugang:	Volltext
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