Improving the Prediction of Scour Depth Downstream of the Flip Bucket with Machine Learning Techniques

Abstract

One of the most common structures used for energy dissipation is flip buckets. The jet passing through these spillways, after being thrown into the air and hitting the downstream bed, still has high energy causing scour downstream of the spillway. Therefore, accurate estimation of the scour depth is important to the proper design of the main and related structures. In recent years, the use of computational intelligence has been widely used to estimate the scour depth accurately. In this research, the maximum scour depth was estimated using three techniques of Gradient Boosting Decision Tree (GBDT), Extra Trees, and Random Forest (RF) and compared with the previous results. The results indicate that the GBDT method with R2=0.992, RMSE=0.231, and MAE=0.180 has the highest accuracy and lowest error.