Developing a generic relation for predicting sediment pick-up rate using symbolic soft computing techniques

Sediment pick-up rate has been investigated using experimental and numerical approaches. However, the use of soft computing methods for its prediction has received less attention so far. In this study, genetic programming (GP), grammatical evolution (GE), and gradient boosting machine (GBM) algorithms are employed to develop a relation in dimensionless form for predicting sediment pick-up rate in open channel flow based on two experimental datasets. Dimensionless Froude number, particle diameter, and depth-averaged turbulent kinetic energy are input variables for prediction. Prediction performance is evaluated with performance indices (root mean square error, mean absolute error, and coefficient of correlation), visual comparisons (scatter, dot, and Bland–Altman plots), and uncertainty indicators (Tsallis and Renyi entropies). Three mathematical expressions for sediment pick-up rate prediction are obtained, with GE producing the most accurate results.