Ibrahim, H., HASHEM, M. (2006). ESTIMATION OF LONGITUDINAL DISPERSION COEFFICIENT IN RIVERS USING ARTIFICIAL NEURAL NETWORKS. JES. Journal of Engineering Sciences, 34(No 5), 1341-1352. doi: 10.21608/jesaun.2006.111026
Hassan Ibrahim; M. HASHEM. "ESTIMATION OF LONGITUDINAL DISPERSION COEFFICIENT IN RIVERS USING ARTIFICIAL NEURAL NETWORKS". JES. Journal of Engineering Sciences, 34, No 5, 2006, 1341-1352. doi: 10.21608/jesaun.2006.111026
Ibrahim, H., HASHEM, M. (2006). 'ESTIMATION OF LONGITUDINAL DISPERSION COEFFICIENT IN RIVERS USING ARTIFICIAL NEURAL NETWORKS', JES. Journal of Engineering Sciences, 34(No 5), pp. 1341-1352. doi: 10.21608/jesaun.2006.111026
Ibrahim, H., HASHEM, M. ESTIMATION OF LONGITUDINAL DISPERSION COEFFICIENT IN RIVERS USING ARTIFICIAL NEURAL NETWORKS. JES. Journal of Engineering Sciences, 2006; 34(No 5): 1341-1352. doi: 10.21608/jesaun.2006.111026
ESTIMATION OF LONGITUDINAL DISPERSION COEFFICIENT IN RIVERS USING ARTIFICIAL NEURAL NETWORKS
Civil Engineering Department, Faculty of Engineering, Assiut University, Assiut, Egypt
Abstract
This study presents an artificial neural network (ANN) model to predict the values of the longitudinal dispersion coefficient in rivers and streams from their main hydraulic parameters. The model can be considered as a useful aid to water quality and sediment transport 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 longitudinal dispersion coefficient. The used ANN model is based on a back propagation algorithm to train a multi-layer feed-forward 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 longitudinal dispersion coefficient, 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 longitudinal dispersion coefficient in polluted rivers and streams that based on readily measurable hydraulic quantities is presented.
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