Neural network prediction of bed material load transport

Bed material load, which comprises bed load and suspended load, has been extensively studied in the past few decades and many equations have been developed, but they differ from each other in derivation and form. If a process can be related to various flow conditions on a general basis, a proper understanding of bed material load movement can be ascertained. As the process is extremely complex, obtaining a deterministic or analytical form of it is too difficult. Neural network modelling, which is particularly useful in modelling processes about which knowledge of the physics is limited, is presented here as a complimentary tool for modelling bed material load transport. The developed model demonstrated a superior performance compared to other traditional methods based on different statistical criteria, such as the coefficient of determination, Nash-Sutcliffe coefficient and discrepancy ratio. The significance of the different input parameters has been analysed in the present work to understand the influence of these parameters on the transport process.