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Suspended Sediment Model in Rivers Using Artificial Neural Networks

Estimating the sediment being transported by river flow is one of the important aspects in water resources engineering. Erosion and sediment transport phenomena in watersheds and rivers are complex hydrodynamic problems. Due to large number of obscure parameters involved in these phenomena, the theoretical governing equations may not be of much advantage in gaining knowledge of the overall process. Researchers have developed practical techniques that do not require much theory, algorithm, or rule development, and thus, reduce the complexities of the problem. One such technique is known as Artificial Neural Networks (ANN). In this paper, Auto-Regressive ANN was utilized to estimate suspended sediment lood in rivers. Various network topology, data partitioning and parameters were examined to find the best network with the best results. For increasing the efficiency of the models, Early Stopping technique has been used. Results of these networks were compared to the conventional sediment rating curves method and it was shown that ANN presented better results especially in peak flow discharges. Trained networks were able to model the sediment transport phenomena in rivers successfully, presumably because of the superior capability of ANN in nonlinear mapping, without any extra information from governing equation.