期刊论文详细信息
Applied Water Science 卷:7
Design of a fuzzy differential evolution algorithm to predict non-deposition sediment transport
Hossein Bonakdari1  Isa Ebtehaj1 
[1] Department of Civil Engineering, Razi University;
关键词: ANFIS;    Bed load;    Differential Evolution (DE);    Non-deposition;    Pipe;    Sediment transport;   
DOI  :  10.1007/s13201-017-0562-0
来源: DOAJ
【 摘 要 】

Abstract Since the flow entering a sewer contains solid matter, deposition at the bottom of the channel is inevitable. It is difficult to understand the complex, three-dimensional mechanism of sediment transport in sewer pipelines. Therefore, a method to estimate the limiting velocity is necessary for optimal designs. Due to the inability of gradient-based algorithms to train Adaptive Neuro-Fuzzy Inference Systems (ANFIS) for non-deposition sediment transport prediction, a new hybrid ANFIS method based on a differential evolutionary algorithm (ANFIS-DE) is developed. The training and testing performance of ANFIS-DE is evaluated using a wide range of dimensionless parameters gathered from the literature. The input combination used to estimate the densimetric Froude number (Fr) parameters includes the volumetric sediment concentration (C V ), ratio of median particle diameter to hydraulic radius (d/R), ratio of median particle diameter to pipe diameter (d/D) and overall friction factor of sediment (λ s ). The testing results are compared with the ANFIS model and regression-based equation results. The ANFIS-DE technique predicted sediment transport at limit of deposition with lower root mean square error (RMSE = 0.323) and mean absolute percentage of error (MAPE = 0.065) and higher accuracy (R 2 = 0.965) than the ANFIS model and regression-based equations.

【 授权许可】

Unknown   

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