Prediction of Bed Load Transport in Heterogeneous sand bed Channel Using Artificial Neural Networks (ANN)

Abstract

The study and estimation of river bed load transport fluctuation is very complex for supplementing existing sediment transport theory. Fluctuation of bed load depends on several scales, including individual particle motion, the development of bed forms, the presence of bed load sheets, and waves of stored sediment. The essential characteristic of a river channel is to percolate the water in terms of downward seepage because of the permeability of sandy materials. Further, the problem of seepage is crucial for the sustainability of hydraulic structures because of its interaction with groundwater. In the present work, we consider the seepage effect on bedload transport across a heterogeneous (non-uniform) sand bed channel based on the importance of downward seepage. The dimension of the channels is 17.20 m in length, 1 m in width and 0.22m in depth. The fact that the bedload transfer rate increases fast and then plateaus is an important finding. The use of neural network modelling, which is particularly beneficial in modelling processes, is described here as a supplement to modelling bed material load transport. In comparison to other traditional methods based on different statistical criteria, the proposed model outperformed them. The significance of the various input characteristics has been investigated in this study to better understand their impact on the transport process.