Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Prediction of Scour Depth below River Pipeline using Support Vector Machine
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
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
Parsaie, Abbas (Autor:in) / Haghiabi, Amir Hamzeh (Autor:in) / Moradinejad, Amir (Autor:in)
KSCE Journal of Civil Engineering ; 23 ; 2503-2513
20.03.2019
11 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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