Abstract
An accurate prediction of the friction coefficient is very important in hydraulic engineering since it directly affects the design of water structures, the calculation of velocity distribution, and an accurate determination of energy losses. However, conventional approaches that are profoundly based on empirical methods lack in providing high accuracy for the prediction of the friction coefficient. Consequently, new and accurate techniques are still highly demanded. This study introduces an efficient approach to estimate the friction coefficient via an artificial neural network, which is a promising computational tool in civil engineering. The estimated value of the friction coefficient is used in Manning Equation to predict the open channel flows in order to carry out a comparison between the proposed neural networks based approach and the conventional ones. Results show that the proposed approach is in good agreement with the experimental results when compared to the conventional ones.
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