Predicting time-dependent pier scour depth with support vector regression

The temporal variation of local pier scour depth is very complex, especially for cases where the bed comprises a sediment mixture. Many semi-empirical models have been proposed to predict the time-dependent local pier scour depth. In this paper, an alternative approach, the support vector regression method (SVR) is used to estimate the temporal variation of pier-scour depth with non-uniform sediments under clear-water conditions. Based on dimensional analyses, the temporal variation of scour depth was modeled as a function of seven dimensionless input parameters, namely flow shallowness (y/Dp ), sediment coarseness (Dp /d 50), densimetric Froude number (Fd ), the difference between the actual and critical densimetric Froude number (Fd − Fdβ ), geometric standard deviation of the sediment particle size distribution (σg ), pier Froude number ( U / gD p ) and one of the following three dimensionless time scales (T 1 = t/tR 1, T 2 = t/tR 2 and T 3 = t/tR 3). The SVR model not only estimates the time-dependent scour depth more accurately than conventional regression models, but also provides results that are consistent with the physics of the scouring process.