Abstract In this paper, the depth of scouring phenomenon below the pipelines across rivers was predicted using Support Vector Machine (SVM). To this end, the related dataset was collected from literature. Performance of SVM was evaluated via calculation of error indices such as coefficient of determination (R2) and Root Mean Square of Error (RMSE). The accuracy of SVM was compared with artificial neural network (ANN) and Adaptive Neuro fuzzy Inference Systems (ANFIS). To find out the most effective parameters on scouring depth, a sensitivity analysis was conducted on ANN, ANFIS, and SVM. During the development of SVM, it was found that this model with R2= 0.94 and RMSE = 0.103 in testing stage has a suitable performance for predicting the scouring depth below the river pipeline. Assessing kernel functions showed that radial basis function has the best outcomes. Comparing the accuracy of SVM with ANN and SVM showed that the accuracy of SVM is a bit better than ANN with R2 = 0.89 and RMSE = 0.12 and ANFIS with R2 = 0.92 and RMSE = 0.13. Sensitivity analysis showed that e/D, τ* and Fr are the most effective parameters for predicting the scouring depth below the pipeline.
Prediction of Scour Depth below River Pipeline using Support Vector Machine
KSCE Journal of Civil Engineering ; 23 , 6 ; 2503-2513
2019-03-20
11 pages
Article (Journal)
Electronic Resource
English
Prediction of Scour Depth below River Pipeline using Support Vector Machine
Online Contents | 2019
|Scour Below Submarine Pipeline Due To Currents
HENRY – Federal Waterways Engineering and Research Institute (BAW) | 2004
|Numerical model for wave-induced scour below a piggyback pipeline
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|Numerical Simulation of Local Scour Below a Vibrating Pipeline in Currents
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|Prediction of Local Scour Below Ofshore Pipelines- A Review
HENRY – Federal Waterways Engineering and Research Institute (BAW) | 2002
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