Modelling of suspended sediment - In Nile river using ANN

Artificial neural network (ANN) prediction models can be considered as an efficient tool in predictions once they are trained from examples or patterns. These types of ANN models need large amount of data which should be at hand before thinking to develop such models. In this paper, the capability of ANN model to predict suspended sediment in 2-D flow field is investigated. The data used for training the network are generated from a pre-verified 2-D hydrodynamic and a 2-D suspended sediment models which were recently developed by the authors. About two-thirds of the data are used for training the network while the rest of the data are used for validating and testing the developed ANN model. Field data measured by hydraulic research Institute are used to compare the results of the ANN model. The conjugate gradient learning algorithm is adopted. The results of the developed ANN model proved that the technique is reliable in such field compared to both the results of the previously developed models and the field data provided that the trained network is used to generate prediction within the range of training data.