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.
Modelling boundary shear stress distribution in open channels using a face recognition technique
Pedro Martinez-Vazquez, Soroosh Sharifi; Modelling boundary shear stress distribution in open channels using a face recognition technique. Journal of Hydroinformatics 1 March 2017; 19 (2): 157–172. doi: https://doi.org/10.2166/hydro.2016.068
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