Application of artificial neural networks to predict flow velocity in a 180° sharp bend with and without a spur dike

This work has compared the performance of three well-known artificial neural network (ANN) approaches in turbulent flow pattern modeling based on geometric characteristics of the channel (angle of horizon, distance from outer bank of the bend, and distance from the bed) in a 180° sharp bend with and without a T-shaped spur dike. ANN methods discussed in this work are feed-forward neural network, cascade feed-forward neural network, and extreme learning machines. Acoustic Doppler velocimetry is used to measure the velocity components in x, y, and z directions. Conducting this research is innovative and significant from three aspects: First, the data of the flow pattern around T-shaped spur dikes in a 180° bend, given its high importance in rivers in nature, are very rarely available; second, application of neural network models for understanding the flow nature is highly efficient at other bend points where no experimental or field measurements have been conducted; and third, with these models, the cost and time required for conducting numerical and experimental modeling for prediction of the flow velocity significantly decrease. Outliers (abnormalities or anomalies) may be generated by various factors in the measured velocities. Before modeling by ANNs, the self-organizing map clustering method is used to detect outliers to obtain more accurate models. Performance of ANN models is evaluated using correlation coefficient (R) and root mean squared error criteria. In order to compare the performance of ANNs in flow velocity modeling at different locations of the bend, seven different data groups are considered using different combinations of samples by random sampling. Results of comparison of ANN models with experimental data indicate that ANNs provide reasonable results in most cases and may be employed successfully in estimating the velocity in sharp bends with and without the presence of a spur dike. Moreover, it may be concluded that ANN models without spur dikes are more accurate than those with spur dikes due to creating less turbulence flow.