scholarly journals Prediction of Boundary Shear Stress Distribution in Converging Compound Channel Using Gene Expression Programming

Author(s):  
Bandita Naik ◽  
Vijay Kaushik ◽  
Munendra Kumar

Abstract The computation of the boundary shear stress distribution in an open channel flow is required for a variety of applications, including the flow resistance relationship and the construction of stable channels. The river breaches the main channel and spills across the floodplain during overbank flow conditions on both sides. Due to the momentum shift between the primary channel and adjacent floodplains, the flow structure in such compound channels becomes complicated. This has a profound impact on the shear stress distribution in the floodplain and main channel subsections. In addition, agriculture and development activities have occurred in floodplain parts of a river system. As a consequence, the geometry of the floodplain changes over the length of the flow, resulting in a converging compound channel. Traditional formulas, which rely heavily on empirical approaches, are ineffective in predicting shear force distribution with high precision. As a result, innovative and precise approaches are still in great demand. The boundary shear force carried by floodplains is estimated by gene expression programming (GEP) in this paper. In terms of non-dimensional geometric and flow variables, a novel equation is constructed to forecast boundary shear force distribution. The proposed GEP-based method is found to be best when compared to conventional methods. The findings indicate that the predicted percentage shear force carried by floodplains determined using GEP is in good agreement with the experimental data compared to the conventional formulas (R2 = 0.96 and RMSE = 3.395 for the training data and R2 = 0.95 and RMSE = 4.022 for the testing data).

2016 ◽  
Vol 19 (2) ◽  
pp. 157-172 ◽  
Author(s):  
Pedro Martinez-Vazquez ◽  
Soroosh Sharifi

This paper describes a novel application of a pattern recognition technique for predicting boundary shear stress distribution in open channels. In this approach, a synthetic database of images representing normalized shear stress distributions is formed from a training data set using recurrence plot (RP) analysis. The face recognition algorithm is then employed to synthesize the RPs and transform the original database into short-dimension vectors containing similarity weights proportional to the principal components of the distribution of images. These vectors capture the intrinsic properties of the boundary shear stress distribution of the cases in the training set, and are sensitive to variations of the corresponding hydraulic parameters. The process of transforming one-dimensional data series into vectors of weights is invertible, and therefore, shear stress distributions for unseen cases can be predicted. The developed method is applied to predict boundary shear stress distributions in smooth trapezoidal and circular channels and the results show a cross correlation coefficient above 92%, mean square errors within 0.04% and 4.48%, respectively, and average shear stress fluctuations within 2% and 5%, respectively, thus indicating that the proposed method is capable of providing accurate estimations of the boundary shear stress distribution in open channels.


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