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Genetic Programming to Predict Spillway Scour
Investigators in the past had noticed that application of a soft computing tool like artificial neural networks (ANN) in place of traditional statistics based data mining techniques produce more attractive results in hydrologic as well as hydraulic predictions. Mostly these works pertained to applications of ANN. Recently another tool of soft computing namely genetic programming (GP) has caught attention of researchers in civil engineering computing. This paper examines the usefulness of the GP based approach to predict the depth and geometry of the scour hole produced downstream of a common type of spillway, namely, the ski-jump bucket. Hydraulic model measurements were used to develop the GP models. The GP based estimations were found to be equally, and possibly more, accurate than the ANN based ones, especially when the underlying cause-effect relationship became more uncertain to model.
Genetic programming, neural networks, scour predictions spillway scour, ski-jump bucket
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