An Artificial Neural Network Model for Predicting Longitudinal Dispersion Coefficients in Rivers

This study presents an artificial neural network (ANN) model to predict the values of the longitudinal dispersion coefficient ) ( l D in rivers from their main hydraulic parameters. The model can be considered as a useful aid to water quality monitoring in rivers. The ANN model is a relatively new promising technique which can make use of the river width, depth, velocity, and shear velocity for predicting l D . The used ANN model is based on a back propagation algorithm to train a multi-layer feedforward network. The proposed model was verified using 116 sets of field data collected from 62 streams ranging from straight manmade canals to sinuous natural rivers. The ANN model predicts l D , where more than 83% of the calculated values range from 0.50 to 2.0 times the observed values in the field. A comparison of the ANN model estimates with the outputs of the most recent and accurate equations in the literature, for the longitudinal dispersion coefficient, using three different statistical methods for analysis, has shown that the accuracy of the ANN model compared favourably with other equations. Finally, a new accurate predictor for the values of l D in polluted streams that based on readily measurable hydraulic quantities is presented.