Discharge Coefficient Estimation of Sharp-Crested Curved-Plan Spillways using Soft Computing Approaches
The use of labyrinth and side weirs and the design of curved-plan form weirs are inevitable in some cases due to design and implementation limitations. The present study proposes dimensionless equations based on the vertex angle and the head ratio over the spillway to the spillway height to estimate...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Pouyan Press
2023-04-01
|
| Series: | Computational Engineering and Physical Modeling |
| Subjects: | |
| Online Access: | https://www.jcepm.com/article_185813_4c16ffe8f3670295b70ac2e68f085d09.pdf |
| _version_ | 1826577604539318272 |
|---|---|
| author | Ali Foroudi Reza Barati |
| author_facet | Ali Foroudi Reza Barati |
| author_sort | Ali Foroudi |
| collection | DOAJ |
| description | The use of labyrinth and side weirs and the design of curved-plan form weirs are inevitable in some cases due to design and implementation limitations. The present study proposes dimensionless equations based on the vertex angle and the head ratio over the spillway to the spillway height to estimate the discharge coefficient of such weirs using the gene expression programming (GEP). Moreover, several other soft computing techniques including adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN), and support vector machine (SVM) approaches have been adopted. Three performance evaluation criteria including Nash-Sutcliffe index (NS), Root Mean Square Error (RMSE), and Mean Normalized Error (MNE) have been used for comparison purposes. To calibrate and validate the proposed models, available experimental data were collected and reanalyzed. A comparison of the calculated and experimental discharge coefficients of curved weirs revealed that the proposed equation had satisfactory accuracy. For the training dataset, the ANFIS, SVM ANN, and GEP have the NS values 0.971, 0.965, 0.962, 0.956, respectively. Moreover, the results of these models were compared with available equation of the prediction of the discharge coefficients of sharp-crested curved-plan form weirs, and it was find that the ANFIS and GEP respectively give 609% and 500% improved results than the existing model in terms of RMSE. |
| first_indexed | 2024-04-25T00:10:33Z |
| format | Article |
| id | doaj.art-bad4c6afcde049538c18c3d65ce8760e |
| institution | Directory Open Access Journal |
| issn | 2588-6959 |
| language | English |
| last_indexed | 2025-03-14T13:49:08Z |
| publishDate | 2023-04-01 |
| publisher | Pouyan Press |
| record_format | Article |
| series | Computational Engineering and Physical Modeling |
| spelling | doaj.art-bad4c6afcde049538c18c3d65ce8760e2025-02-27T15:51:27ZengPouyan PressComputational Engineering and Physical Modeling2588-69592023-04-0162234210.22115/cepm.2023.428925.1263185813Discharge Coefficient Estimation of Sharp-Crested Curved-Plan Spillways using Soft Computing ApproachesAli Foroudi0Reza Barati1Assistant Professor, Civil Engineering Department, Quchan University of Technology, Quchan, IranHead of Applied Research Department, Khorasan Razavi Regional Water Company, IranThe use of labyrinth and side weirs and the design of curved-plan form weirs are inevitable in some cases due to design and implementation limitations. The present study proposes dimensionless equations based on the vertex angle and the head ratio over the spillway to the spillway height to estimate the discharge coefficient of such weirs using the gene expression programming (GEP). Moreover, several other soft computing techniques including adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN), and support vector machine (SVM) approaches have been adopted. Three performance evaluation criteria including Nash-Sutcliffe index (NS), Root Mean Square Error (RMSE), and Mean Normalized Error (MNE) have been used for comparison purposes. To calibrate and validate the proposed models, available experimental data were collected and reanalyzed. A comparison of the calculated and experimental discharge coefficients of curved weirs revealed that the proposed equation had satisfactory accuracy. For the training dataset, the ANFIS, SVM ANN, and GEP have the NS values 0.971, 0.965, 0.962, 0.956, respectively. Moreover, the results of these models were compared with available equation of the prediction of the discharge coefficients of sharp-crested curved-plan form weirs, and it was find that the ANFIS and GEP respectively give 609% and 500% improved results than the existing model in terms of RMSE.https://www.jcepm.com/article_185813_4c16ffe8f3670295b70ac2e68f085d09.pdfdischarge coefficientsharp-crested weirscurve axisartificial intelligence |
| spellingShingle | Ali Foroudi Reza Barati Discharge Coefficient Estimation of Sharp-Crested Curved-Plan Spillways using Soft Computing Approaches Computational Engineering and Physical Modeling discharge coefficient sharp-crested weirs curve axis artificial intelligence |
| title | Discharge Coefficient Estimation of Sharp-Crested Curved-Plan Spillways using Soft Computing Approaches |
| title_full | Discharge Coefficient Estimation of Sharp-Crested Curved-Plan Spillways using Soft Computing Approaches |
| title_fullStr | Discharge Coefficient Estimation of Sharp-Crested Curved-Plan Spillways using Soft Computing Approaches |
| title_full_unstemmed | Discharge Coefficient Estimation of Sharp-Crested Curved-Plan Spillways using Soft Computing Approaches |
| title_short | Discharge Coefficient Estimation of Sharp-Crested Curved-Plan Spillways using Soft Computing Approaches |
| title_sort | discharge coefficient estimation of sharp crested curved plan spillways using soft computing approaches |
| topic | discharge coefficient sharp-crested weirs curve axis artificial intelligence |
| url | https://www.jcepm.com/article_185813_4c16ffe8f3670295b70ac2e68f085d09.pdf |
| work_keys_str_mv | AT aliforoudi dischargecoefficientestimationofsharpcrestedcurvedplanspillwaysusingsoftcomputingapproaches AT rezabarati dischargecoefficientestimationofsharpcrestedcurvedplanspillwaysusingsoftcomputingapproaches |