Civil Engineering, Urmia University
Engineering Faculty, Urmia University
Civil Engineering, Pars Special Econemic Energy Zon
An intelligent method based on adaptive neuro-fuzzy inference system (ANFIS) for identifying Manning’s roughness coefficients in modeling of alluvial river is presented. The procedure for selecting values of Manning n is subjective and requires judgment and skill which are developed primarily through experience. During practical applications, researchers often find that a correct choice of the Manning n can be crucial to make a sound prediction of hydraulic problems. In this paper, an ANFIS model was set up to predict the Manning coefficient of alluvial river, with the mean bed particle size, mean flow depth and channel slope as three input parameters. The regression equations are also applied to the same data. Statistic measures were used to evaluate the performance of the models. Based on comparison of the results, it is found that the ANFIS model gives better estimates than the other empirical relationships. Also, a sensitivity analysis showed that mean flow depth has a greater influence on Manning coefficient than the other independent parameters in ANFIS model.