A model of adaptive neural-based fuzzy inference system (ANFIS) for prediction of friction coefficient in open channel flow

Friction coefficient factor in free surface smooth channel with rectangular cross-section is generally affected by Reynolds number and wall roughness and can be determined with laboratory or field measurements. In application, according to researchers, correct selection of friction coefficient is substantially critical to estimate hydraulic problems correctly. In this paper an ANFIS is set up in which Reynolds number, velocity and discharge are used as inputs to estimate friction coefficients of an open channel flow (FC). By using experimental data from the laboratory, learning algorithm and training are applied according to ANFIS model. As a result, simulation results are compared with experimental friction coefficient results. A good correlation is obtained between the experimental data and predicted results. It is shown that when provided with correct and sufficient samples, ANFIS model can be used to predict the non-linear relationship between friction coefficient and the factors which affect it. It is concluded that, in practice, ANFIS model can be used as a suitable and effective method and general hydraulic problems which are mostly based on laboratory tests can be analyzed with ANFIS model.   Key words: Open channel, friction coefficient, ANFIS.

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