What is it about?
This article compares the estimation of critical/minimum velocity for sediment particle from equations developed from conventional regression methods and new hybrid artificial neural network method.
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Why is it important?
We found that the hybrid artificial neural network method via MLP-DT (multilayer perceptron - decision tree) can improve the prediction of critical velocity as compared to the equations developed from conventional regression method.
Perspectives
Soft computing tools such as artificial neural network can improve the prediction power of the developed equation. The only drawback is that the developed equations may be too lengthy to be practically used by design engineer who prefer simple equations.
Assoc. Prof. Ir. Dr. Charles H.J. Bong
Universiti Malaysia Sarawak
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This page is a summary of: Design of a new hybrid artificial neural network method based on decision trees for calculating the Froude number in rigid rectangular channels, Journal of Hydrology and Hydromechanics, January 2016, De Gruyter,
DOI: 10.1515/johh-2016-0031.
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