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Prediction of longitudinal dispersion coefficients in natural rivers using artificial neural network

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Abstract

An artificial neural network (ANN) model is developed for predicting the longitudinal dispersion coefficient in natural rivers. The model uses few rivers’ hydraulic and geometric characteristics, that are readily available, as the model input, and the target output is the longitudinal dispersion coefficient (K). For performance evaluation of the model, using published field data, predictions by the developed ANN model are compared with those of other reported important models. Based on various performance indices, it is concluded that the new model predicts the longitudinal dispersion coefficient more accurately. Sensitive analysis performed on input parameters indicates stream width, flow depth, stream sinuosity, flow velocity, and shear velocity to be the most influencing parameters for accurate prediction of the longitudinal dispersion coefficient.

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Authors and Affiliations

  1. Department of Civil Engineering, Birla Institute of Technology, Mesra, Ranchi, 835215, India

    Rajeev Ranjan Sahay

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  1. Rajeev Ranjan Sahay
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Correspondence to Rajeev Ranjan Sahay.

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Sahay, R.R. Prediction of longitudinal dispersion coefficients in natural rivers using artificial neural network. Environ Fluid Mech 11, 247–261 (2011). https://doi.org/10.1007/s10652-010-9175-y

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  • DOI: https://doi.org/10.1007/s10652-010-9175-y

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