Experimental investigation and application of soft computing models for predicting flow energy loss in arc-shaped constrictions
This investigation focuses on flow energy, a crucial parameter in the design of water structures such as channels. The research endeavors to explore the relative energy loss (ΔEAB/EA) in a constricted flow path of varying widths, employing Support Vector Machine (SVM), Artificial Neural Network (ANN...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IWA Publishing
2024-03-01
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| Series: | Aqua |
| Subjects: | |
| Online Access: | http://aqua.iwaponline.com/content/73/3/637 |
| _version_ | 1797205458565464064 |
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| author | Hamidreza Abbaszadeh Rasoul Daneshfaraz Veli Sume John Abraham |
| author_facet | Hamidreza Abbaszadeh Rasoul Daneshfaraz Veli Sume John Abraham |
| author_sort | Hamidreza Abbaszadeh |
| collection | DOAJ |
| description | This investigation focuses on flow energy, a crucial parameter in the design of water structures such as channels. The research endeavors to explore the relative energy loss (ΔEAB/EA) in a constricted flow path of varying widths, employing Support Vector Machine (SVM), Artificial Neural Network (ANN), Gene Expression Programming (GEP), Multiple Adaptive Regression Splines (MARS), M5 and Random Forest (RF) models. Experiments span a Froude number range from 2.85 to 8.85. The experimental findings indicate that the ΔEAB/EA exceeds that observed in a classical hydraulic jump with constriction section. Within the SVM model, the linear kernel emerges as the best predictor of ΔEAB/EA, outperforming polynomial, radial basis function (RBF), and sigmoid kernels. In addition, in the ANN model, the MLP network was more accurate compared to the RBF network. The results indicate that the relationship proposed by the MARS model can play a significant role resulting in high accuracy compared to the non-linear regression relationship in predicting the target parameter. Upon comprehensive evaluation, the ANN method emerges as the most promising among the candidates, yielding superior performance compared to the other models. The testing phase results for the ANN-MLP are noteworthy, with R = 0.997, average RE% = 0.63%, RMSE = 0.0069, BIAS = −0.0004, DR = 0.999, SI = 0.0098 and KGE = 0.995.
HIGHLIGHTS
This research reinforces the important of investigating the effect of arc-shaped constrictions in the flow path (such as constrictions from bridge piers).;
This investigation improves the design of hydraulic control structures.;
The performance of the ANN, GEP, MARS, M5, RF, SVM, and regression models has been evaluated using quantitative and qualitative indices (KGE, R, RE%, RMSE, BIAS, DR, scatter index (SI)).; |
| first_indexed | 2024-04-24T08:51:27Z |
| format | Article |
| id | doaj.art-d81b2b0973f94b638a3a3d18c717a13d |
| institution | Directory Open Access Journal |
| issn | 2709-8028 2709-8036 |
| language | English |
| last_indexed | 2024-04-24T08:51:27Z |
| publishDate | 2024-03-01 |
| publisher | IWA Publishing |
| record_format | Article |
| series | Aqua |
| spelling | doaj.art-d81b2b0973f94b638a3a3d18c717a13d2024-04-16T11:13:27ZengIWA PublishingAqua2709-80282709-80362024-03-0173363766110.2166/aqua.2024.010010Experimental investigation and application of soft computing models for predicting flow energy loss in arc-shaped constrictionsHamidreza Abbaszadeh0Rasoul Daneshfaraz1Veli Sume2John Abraham3 Department of Civil Engineering, Faculty of Civil Engineering, University of Tabriz, Tabriz, Iran Department of Civil Engineering, Faculty of Engineering, University of Maragheh, Maragheh, Iran Department of Civil Engineering, Faculty of Engineering and Architecture, Recep Tayyip Erdogan University, Rize, Turkiye School of Engineering, University of St. Thomas, St. Paul, MN 33901, USA This investigation focuses on flow energy, a crucial parameter in the design of water structures such as channels. The research endeavors to explore the relative energy loss (ΔEAB/EA) in a constricted flow path of varying widths, employing Support Vector Machine (SVM), Artificial Neural Network (ANN), Gene Expression Programming (GEP), Multiple Adaptive Regression Splines (MARS), M5 and Random Forest (RF) models. Experiments span a Froude number range from 2.85 to 8.85. The experimental findings indicate that the ΔEAB/EA exceeds that observed in a classical hydraulic jump with constriction section. Within the SVM model, the linear kernel emerges as the best predictor of ΔEAB/EA, outperforming polynomial, radial basis function (RBF), and sigmoid kernels. In addition, in the ANN model, the MLP network was more accurate compared to the RBF network. The results indicate that the relationship proposed by the MARS model can play a significant role resulting in high accuracy compared to the non-linear regression relationship in predicting the target parameter. Upon comprehensive evaluation, the ANN method emerges as the most promising among the candidates, yielding superior performance compared to the other models. The testing phase results for the ANN-MLP are noteworthy, with R = 0.997, average RE% = 0.63%, RMSE = 0.0069, BIAS = −0.0004, DR = 0.999, SI = 0.0098 and KGE = 0.995. HIGHLIGHTS This research reinforces the important of investigating the effect of arc-shaped constrictions in the flow path (such as constrictions from bridge piers).; This investigation improves the design of hydraulic control structures.; The performance of the ANN, GEP, MARS, M5, RF, SVM, and regression models has been evaluated using quantitative and qualitative indices (KGE, R, RE%, RMSE, BIAS, DR, scatter index (SI)).;http://aqua.iwaponline.com/content/73/3/637artificial intelligenceconstricted arc-shapedenergy lossnonlinear regression |
| spellingShingle | Hamidreza Abbaszadeh Rasoul Daneshfaraz Veli Sume John Abraham Experimental investigation and application of soft computing models for predicting flow energy loss in arc-shaped constrictions Aqua artificial intelligence constricted arc-shaped energy loss nonlinear regression |
| title | Experimental investigation and application of soft computing models for predicting flow energy loss in arc-shaped constrictions |
| title_full | Experimental investigation and application of soft computing models for predicting flow energy loss in arc-shaped constrictions |
| title_fullStr | Experimental investigation and application of soft computing models for predicting flow energy loss in arc-shaped constrictions |
| title_full_unstemmed | Experimental investigation and application of soft computing models for predicting flow energy loss in arc-shaped constrictions |
| title_short | Experimental investigation and application of soft computing models for predicting flow energy loss in arc-shaped constrictions |
| title_sort | experimental investigation and application of soft computing models for predicting flow energy loss in arc shaped constrictions |
| topic | artificial intelligence constricted arc-shaped energy loss nonlinear regression |
| url | http://aqua.iwaponline.com/content/73/3/637 |
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