Genetic programming to predict ski-jump bucket spill-way scour

Researchers in the past had noticed that application of Artificial Neural Networks (ANN) in place of conventional statistics on the basis of data mining techniques predicts more accurate results in hydraulic predictions. Mostly these works pertained to applications of ANN. Recently, another tool of...

Full description

Saved in:
Bibliographic Details
Published in:Journal of Hydrodynamics 2008-08, Vol.20 (4), p.477-484
Main Authors: MD. AZAMATHULLA, H., AB. GHANI, A., ZAKARIA, N.A., LAI, S.H., CHANG, C.K., LEOW, C.S., ABUHASAN, Z.
Format: Article
Language:English
Subjects:
Engineering
Engineering Fluid Dynamics
Genetic Programming (GP)
Hydrology/Water Resources
neural networks
Numerical and Computational Physics
Simulation
ski-jump bucket
spillway scour
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Researchers in the past had noticed that application of Artificial Neural Networks (ANN) in place of conventional statistics on the basis of data mining techniques predicts more accurate results in hydraulic predictions. Mostly these works pertained to applications of ANN. Recently, another tool of soft computing, namely, Genetic Programming (GP) has caught the attention of researchers in civil engineering computing. This article examines the usefulness of the GP based approach to predict the relative scour depth downstream of a common type of ski-jump bucket spillway. Actual field measurements were used to develop the GP model. The GP based estimations were found to be equally and more accurate than the ANN based ones, especially, when the underlying cause-effect relationship became more uncertain to model.
ISSN:1001-6058
1878-0342
1000-4874
DOI:10.1016/S1001-6058(08)60083-9