Application of ANN in Estimating Discharge Coefficient of Circular Piano Key Spillways

Abstract

Among all solutions for disrupted vortex formation in shaft spillways, an innovative one called Circular Piano Key Spillway, based upon piano key weir principles, has been experimented less. In this study, the potential of Artificial Neural Networks (ANN) in estimating the amounts of discharge coefficient of Circular Piano Key Spillway has been evaluated. In order to pursue this purpose, the results of some physical experiments were used. These experiments have been conducted in the hydraulic laboratory using different physical models of Circular Piano Key Spillway including three models with different angles of 45, 60 and 90 degrees. Data from those experiments were used in training and test steps of ANN models. Multilayer Perceptron (MLP) network with Levenberg-Marquardt backpropagation algorithm was used. The performance of artificial neural network was measured by these statistical indicators: coefficient of determination (R2), mean absolute error (MAE), root mean square error (RMSE) and mean absolute percentage error (MAPE) and optimum quantities of statistical indicators for test step were assessed 0.9999, 0.4988, 0.5963 and 0.9999 respectively, for Circular Piano Key Spillway with an angle of 90 degree and for training step were assessed 0.9999, 0.5479, 0.6305 and 0.9999 respectively, for Circular Piano Key Spillway with an angle of 90 degree. In other words, Circular Piano Key Spillway with an angle of 90 degrees has the optimum performance, both in training and test steps. Artificial Neural Network model can successfully estimate the amounts of discharge coefficient of Circular Piano Key Spillway.