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 the flip bucket. A 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 in 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 the three techniques of ‘gradient boosting decision tree’ (GBDT), ‘extra trees’ and ‘random forest’, then compared with previous results. The results indicate that the GBDT method, with an R2 of 0.992, a root mean square error of 0.231 and mean absolute error of 0.180, has the highest accuracy and lowest error.