Optimizing ANFIS for sediment transport in open channels using different evolutionary algorithms

Publish Year: 1396
نوع سند: مقاله ژورنالی
زبان: English
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JR_ARWW-4-1_002

تاریخ نمایه سازی: 24 شهریور 1398

Abstract:

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).

Authors

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

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