Friction Factor Prediction of Heterogeneous Channel Using Artificial Neural Network (ANN)

Abstract

An exact forecast of the friction factor coefficient is crucial in hydraulic engineering, because it has a direct impact on the design of hydraulic structures, the computation of velocity distribution, and the precise estimation of energy losses. Friction factor of a channel is a kind of frictional force that prevents the river from moving forward as it flows downhill, and basically it depends on the smoothness or roughness of the channel. This study is an attempt to rank the input parameters that have a major impact on the friction factor. To do so, first data collection is done. Then artificial neural network (ANN) model is created in Python using those data, and then model's performance was tested using regression graphs. Prediction capabilities of various equations proposed by different authors were validated by plotting regression graphs and by comparing the values of coefficients of determination(R2 ). At last, analysis is done by individual graphs, between input and output parameters. The result revealed that the friction factor decreases with increase in flow depth, friction slope, shear velocity, particle size, and flow discharge and discharge has shown a larger impact on the value of friction factor as analyzed from the graphs. The model provided in this study, when compared with other models produced better results when measured on the basis of the value of coefficient of determination.