Discharge coefficient prediction of canal radial gate using neurocomputing models: an investigation of free and submerged flow scenarios
In the current study, three machine learning (ML) models, i.e. Gaussian process regression (GPR), generalized regression neural network (GRNN), and multigene genetic programming (MGGP), were developed for predicting the discharge coefficient (Cd) of a radial gate under two different flow conditions,...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor and Francis Ltd.
2022
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| Series: | Engineering Applications of Computational Fluid Mechanics
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| Subjects: | |
| Online Access: | View Fulltext in Publisher View in Scopus |