Prediction of Flow in Non-prismatic Compound Channels using Adaptive Neuro-Fuzzy Inference System

Abstract

Discharge estimation in rivers is the most important parameter in flood management. Predicting discharge in the non-prismatic compound open channel by analytical approach leads to solving a system of complex nonlinear equations. In many complex mathematical problems that lead to solving complex problems, an artificial intelligence model could be used. In this study, the adaptive neurofuzzy inference system (ANFIS) is used for modeling and predicting of flow discharge in the nonprismatic compound open channel. Comparison of results showed that the divided channel method with vertical division lines with the coefficient of determination (0.73) and root mean square error (0.009) is accurate among the analytical approaches. The non-dimensional parameters like friction factor ratio, area ratio, hydraulic radius ratio, bed slope, width ratio, relative flow depth, angle of converging or diverging, relative longitudinal distance, flow aspect ratio have been taken as input parameters in for predicting discharge. The ANFIS model with a coefficient of determination (0.98) and root mean square error (0.005) for the testing stage has a suitable performance for predicting the discharge in the non-prismatic compound open channel.