Three kernel-based modeling approaches are proposed to predict the local scour around bridge piers using field data. Modeling approaches include Gaussian processes regression (GPR), relevance vector machines (RVM) and a kernlised extreme learning machine (KELM). A dataset consisting of 232 upstream pier scour measurements derived from the Bridge Scour Data Management System (BSDMS) was used. The radial basis kernel function was used with all three kernel-based approaches and results were compared with support vector regression and four empirical relations. Coefficient of determination value of 0.922, 0.922 and 0.900 (root mean square error, RMSE = 0.297, 0.310 and 0.343 m) was achieved by GPR, RVM and KELM algorithm respectively. Comparisons of results with support vector regression and Froehlich equation, Froehlich design, HEC-18 and HEC-18/Mueller predictive equations suggest an improved performance by the proposed approaches. Results with dimensionless data using all three algorithms suggest a better performance by dimensional data.
Research Article|November 13 2013
Kernel methods for pier scour modeling using field data
N. K. Singh
Mahesh Pal, N. K. Singh, N. K. Tiwari; Kernel methods for pier scour modeling using field data. Journal of Hydroinformatics 1 July 2014; 16 (4): 784–796. doi: https://doi.org/10.2166/hydro.2013.024
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