Estimating Transverse Mixing Coefficient of Pollutants in Open Channel Flows Using Artificial Neural Networks

Understanding of the fate of pollutants, disposed of in streams, is a matter of concern in recent years for the effective control of pollution. Transverse mixing of the pollutants in open channels is arguably more important than the longitudinal mixing and near-field mixing. Several attempts have been made to establish the relationship between the transverse mixing coefficient and bulk channel and flow parameters such as width, depth, shear velocity, friction factor, curvature and sinuosity. The training and testing of this model are accomplished using a set of available published filed data. Several statistical and graphical criteria are used to check the accuracy of the model. The proposed ANN approach produces satisfactory results (R2=0.82, RMSE 0.103) in the best try in comparison to linear model.