An evaluation of ANN methods for estimating the lengths of hydraulic jumps in U-shaped channel

Modelling of hydraulic characteristics of jump using theoretical and empirical models has always been a difficult task. The length of jump may be defined as the distance measured from the toe of the jump to the location of the surface rise. Due to high turbulence this length cannot be determined easily by theory. However, it has been investigated experimentally so as to design the stilling basins with hydraulic jumps. In this work, the control of a hydraulic jump by broad-crested sills in a U-shaped channel is recalled theoretically and experimentally examined. The study begins with a multiple regression (MR) analysis. Then, and in order to model the relative lengths of hydraulic jumps, we have implemented and evaluated two different artificial neural networks (ANN): multilayer perceptron neural network (MLPNN) and generalized regression neural network (GRNN). The results demonstrate the predictive strength of GRNN and its potential to predict hydraulic problems with an adaptive spread value. However, the MLPNN model remains best classified by these indexes of performance.