Estimating the energy dissipation of flow passing over triangular and trapezoidal plan weirs using the GMDH model

Abstract

Weirs are one of the most common hydraulic structures used in water engineering projects. In this research, a group method of data handling (GMDH) was developed to estimate the energy dissipation of the flow passing over the labyrinth weirs with triangular and trapezoidal plans. To compare the performance of this model with other types of soft computing models, a multilayer perceptron neural network (MLPNN) was developed. The dimensionless parameters derived from dimensional analysis, including the relative upstream head (ho/P), the number of cycles (Ncy), the Froude number ( Fr), and the magnification ratio (Mr) were used as input variables. The error statistical indicators of the GMDH model in the training phase were R2 = 0.913, RMSE = 0.010, and in the testing phase were R2 = 0.829, RMSE = 0.015. The error statistical indicators of the MLPNN in the training phase were R2 = 0.957, RMSE = 0.007, and in the testing phase were R2 = 0.945, RMSE = 0.009. Examining the structure of the GMDH network shows that ho/P, Ncy, and Mr play more meaningful roles in the development network. HIGHLIGHTS Development of GMDH to predict the energy dissipation of the flow passing over the labyrinth weirs.; Developing the MLPNN model to predict the energy dissipation of the flow passing over the labyrinth weirs.; Definition of most effective parameters on the mechanism of energy dissipation of flow.; Comparison of the performance of MLPNN and GMDH models based on the Taylor diagram.;

Keywords

• gmdh
• labyrinth weir
• magnification ratio
• mlpnn
• soft computing models