Kargar Katayoun, Safari Mir Jafar Sadegh, Mohammadi Mirali, Samadianfard Saeed
Department of Civil Engineering, Urmia University, Urmia, Iran.
Department of Civil Engineering, Yaşar University, Izmir, Turkey E-mail:
Water Sci Technol. 2019 Jun;79(12):2318-2327. doi: 10.2166/wst.2019.229.
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) 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 estimation.
沉积物的淤积是下水道、城市排水、灌溉渠道以及一般刚性边界渠道设计中一个至关重要的经济和技术问题。为了限制沉积物的持续淤积，刚性边界渠道是根据自净标准设计的。最近，在自净渠道设计中，不再使用单一速度值，而是利用更多的水力参数，如沉积物、流体、水流和渠道特性。在本研究中，采用神经模糊（NF）和基因表达式编程（GEP）两种技术来估计刚性边界渠道中沉积物输运非淤积条件下的颗粒弗劳德数（Fr）。这些模型是基于沉积物和管道尺寸范围广泛的实验室实验数据建立的。已根据包括均方根误差（RMSE）和一致性系数（CC）在内的两个统计因素，将所开发模型的性能与经验方程进行了比较。此外，还使用泰勒图来检验测量值与计算值之间的相似性。结果表明，作为精确的NF模型，NF4的性能优于最佳的GEP模型（GEP1）和回归方程。总之，所得结果证明了NF方法在弗劳德数估计中具有合适的精度和适用性。
