Laboratory Investigation of Scour Hole Location Downstream of Dams and its Prediction Using Data Mining Methods

Abstract

Various factors affect the location of the scour hole formed by the falling flow. Among these factors, we can mention the tailwater depth, the height of the falling flow, the velocity of the flow, the cross-section of the flow and the amount of air entering the flow. In the present study, the location of the formation of scour holes was investigated in the hydraulic laboratory of Tabriz University. Also, the ability of artificial neural networks (ANN) and tree models (M5P tree model) in estimating the location of scour holes downstream of dams was investigated using laboratory data and the results of these two models have been compared with the multivariate nonlinear regression method. The results showed that all three methods, the artificial neural network, the M5 tree model and regression method provide relatively accurate results in predicting the location of scour hole. RMSE value for ANN=1.75, M5=3.75 and Regression=3.89, but due to providing simple linear relationships by the M5 tree model, this method can be used as a practical method to determine the location of scour hole. The analysis of the M5 tree model showed that 4 equations with different linear equations model the pattern of changes in the location of scour hole. In addition, the analysis of the laboratory results showed that the regression equations presented in the present study compared to the common method (using projectile equations) have much less error when predicting the location of scour hole. Also, the laboratory results showed that the head passing through the structure is the most effective parameter in the formation of the scour hole.

Keywords

• scouring hole
• m5 tree models
• artificial neural networks
• falling flow
• projectile equation