This paper presents application of five soft-computing techniques, artificial neural networks, support vector Training Tops regression, gene expression programming, grouping method of data handling (GMDH) neural network and adaptive-network-based fuzzy inference system, to predict maximum scour hole depth downstream of a sluice gate.The input parameters affecting the scour depth are the sediment size and its gradation, apron length, sluice gate opening, jet Froude number and the tail water depth.Six non-dimensional parameters were achieved to define a functional relationship between the input and output variables.Published data were used from the experimental researches.
The results of soft-computing techniques were compared with empirical and regression based equations.The results obtained from the soft-computing techniques are ORG NO CHICKEN NOODLE SOUP superior to those of empirical and regression based equations.Comparison of soft-computing techniques showed that accuracy of the ANN model is higher than other models (RMSE = 0.869).
A new GEP based equation was proposed.