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《国际泥沙研究》2020,35(2):157-170
Mitigation of sediment deposition in lined open channels is an essential issue in hydraulic engineering practice.Hence,the limiting velocity should be determined to keep the channel bottom clean from sediment deposits.Recently,sediment transport modeling using various artificial intelligence(AI) techniques has attracted the interest of many researchers.The current integrated study highlights unique insight for modeling of sediment transport in sewer and urban drainage systems.A novel methodology based on the combination of sensitivity and uncertainty analyses with a machine learning technique is proposed as a tool for selection of the best input combination for modeling process at non-deposition conditions of sediment transport.Utilizing one to seven dimensionless parameters,127 models are developed in the current study.In order to evaluate the different parameter co mbinations and select the training and te sting data,four strategies are considered.Considering the densimetric Froude number(Fr) as the dependent parameter,a model with independent parameters of volumetric sediment concentration(C_V) and relative particle size(d/R) gave the best results with a mean absolute relative error(MARE) of 0.1 and a root means square error(RMSE) of 0.67.Uncertainty analysis is applied with a machine learning technique to investigate the credibility of the proposed methods.The percentage of the observed sample data bracketed by95% predicted uncertainty bound(95 PPU) is computed to assess the uncertainty of the best models. 相似文献
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It is known that construction of large sewers based on consideration of flow with non-deposition without a bed deposit is not economical. Sewer design based on consideration of flow with non-deposition with a bed deposit reduces channel bed slope and construction cost in which the presence of a small depth of sediment deposition on the bed increases the sediment transport capacity of the flow. This paper suggests a new Pareto-optimal model developed by the multigene genetic programming (MGGP) technique to estimate particle Froude number (Frp) in large sewers with conditions of sediment deposition on the bed. To this end, four data sets including wide ranges of sediment size and concentration, deposit thickness, and pipe size are used. On the basis of different statistical performance indices, the efficiency of the proposed Pareto-optimal MGGP model is compared to those of the best MGGP model developed in the current study as well as the conventional regression models available in the literature. The results indicate the higher efficiency of the MGGP-based models for Frp estimation in the case of no additional deposition onto a bed with a sediment deposit. Inasmuch as the Pareto-optimal MGGP model utilizes a lower number of input parameters to yield comparatively higher performance than the conventional regression models, it can be used as a parsimonious model for self-cleansing design of large sewers in practice. 相似文献
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