Predicting time-dependent pier scour depth with support vector regression
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...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2013
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| Online Access: | https://hdl.handle.net/10356/96851 http://hdl.handle.net/10220/11643 |
| Summary: | 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. |
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