Sediment transport modeling in open channels using neuro-fuzzy and gene expression programming techniques

Abstract

Deposition of sediment is a vital economical and technical problem for design of sewers urban drainage irrigation channels and in general rigid boundary channels. In order to confine continuous sediment deposition rigid boundary channels are designed based on self-cleansing criteria. Recently instead of using a single velocity value for design of the self-cleansing channels more hydraulic parameters such as sediment fluid flow and channel characteristics are being utilized. In this study two techniques of neuro-fuzzy (NF) and gene expression programming (GEP) are implemented for particle Froude number (Fr<inf>p</inf>) estimation of the non-deposition condition of sediment transport in rigid boundary channels. The models are established based on laboratory experimental data with wide ranges of sediment and pipe sizes. The developed models’ performances have been compared with empirical equations based on two statistical factors comprising the root mean square error (RMSE) and the concordance coefficient (CC). Besides Taylor diagrams are used to test the resemblance between measured and calculated values. The outcomes disclose that NF4 as the precise NF model performs better than the best GEP model (GEP1) and regression equations. As a conclusion the obtained results proved the suitable accuracy and applicability of the NF method in Fr<inf>p</inf> estimation. © 2019 Elsevier B.V. All rights reserved.