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Title: Predicting time-dependent pier scour depth with support vector regression
Authors: Hong, Jian-Hao
Goyal, Manish Kumar
Chiew, Yee-Meng
Chua, Lloyd Hock Chye
Issue Date: 2012
Source: Hong, J.-H., Goyal, M. K., Chiew, Y.-M.,& Chua, L. H. C. (2012). Predicting time-dependent pier scour depth with support vector regression. Journal of Hydrology, 468-469, 241-248.
Series/Report no.: Journal of hydrology
Abstract: The temporal variation of local pier scour depth is very complex, especially for cases where the bed comprises a sediment mixture. Many semi-empirical models have been proposed to predict the time-dependent local pier scour depth. In this paper, an alternative approach, the support vector regression method (SVR) is used to estimate the temporal variation of pier-scour depth with non-uniform sediments under clear-water conditions. Based on dimensional analyses, the temporal variation of scour depth was modeled as a function of seven dimensionless input parameters, namely flow shallowness (y/Dp), sediment coarseness (Dp/d50), densimetric Froude number (Fd), the difference between the actual and critical densimetric Froude number (Fd − Fdβ), geometric standard deviation of the sediment particle size distribution (σg), pier Froude number (U/gDp) and one of the following three dimensionless time scales (T1 = t/tR1, T2 = t/tR2 and T3 = t/tR3). The SVR model not only estimates the time-dependent scour depth more accurately than conventional regression models, but also provides results that are consistent with the physics of the scouring process.
ISSN: 0022-1694
Rights: © 2012 Elsevier B.V.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:CEE Journal Articles

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