Sultan Noman Qasem - Computer Science Department, College of Computer and Information Sciences, Al Imam Mohammad Ibn Saud, Islamic University (IMSIU), Riyadh, Saudi Arabia
Isa Ebtehaj - Young Researchers and Elite Club, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran
Hossien Riahi Madavar - Department of water engineering, Vali-e-Asr University of Rafsanjan, Rafsanjan, Iran

Flow through open channels can contain solids. The deposition of solids occasionally occurs due to insufficient flow velocity to transfer the solid particles, causing many problems with transfer systems. Therefore, a method to determine the limiting velocity (i.e. Fr) is required. In this paper, three alternative, hybrid evolutionary algorithm methods, including differential evolution (DE), genetic algorithm (GA) and particle swarm optimization (PSO) based on the adaptive network-based fuzzy inference system are presented: ANFIS-GA, ANFIS-DE and ANFIS-PSO. In these methods, evolutionary algorithms optimize the membership functions, and ANFIS adjusts the premises and consequent parameters to optimize prediction performance. The performance of the proposed methods is compared with that of the general ANFIS using three different datasets comprising a wide range of data. The results show that the hybrid models (ANFIS-GA, ANFIS-DE and ANFIS-PSO) are more accurate than general ANFIS in training with a hybrid algorithm (hybrid of back propagation and least squares). Among the evolutionary algorithms, ANFIS-PSO performed the best (R2=0.976, RMSE=0.26, MARE=0.057, BIAS=-0.004 and SI=0.059).

ANFIS, Differential Evolution (DE), Genetic Algorithm (GA), non-deposition sediment transport, Particle Swarm Optimization (PSO)