Estimating Coastal Dyke Leakage Flow Using Support Vector Machine (SVM) and Multivariate Adaptive Regression Spline (MARS) Model
It is important to check for leakage flow in hydraulic and marine structures during design, as uncontrolled leakage can cause irreparable damage. Soft computing methods can be used to easily model, analyze and control complex systems. This study uses Support Vector Machine (SVM) method to predict le...
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| Format: | Article |
| Language: | English |
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K. N. Toosi University of Technology
2024-06-01
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| Series: | Numerical Methods in Civil Engineering |
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| Online Access: | https://nmce.kntu.ac.ir/article_200266_1202942f55aa0b2a4bc5a978cd5c5b5e.pdf |
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| _version_ | 1846114738743803904 |
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| author | Adel Asakereh Farhad Choobi mohammad bagherzadeh reza mirzaee |
| author_facet | Adel Asakereh Farhad Choobi mohammad bagherzadeh reza mirzaee |
| author_sort | Adel Asakereh |
| collection | DOAJ |
| description | It is important to check for leakage flow in hydraulic and marine structures during design, as uncontrolled leakage can cause irreparable damage. Soft computing methods can be used to easily model, analyze and control complex systems. This study uses Support Vector Machine (SVM) method to predict leakage discharge of coastal dykes. Five different models are used to achieve this goal, with parameters including the length of the cutoff blanket, dyke depth, and water head considered. The best support vector machine model is checked using a multivariate adaptive regression spline model (MARS) for prediction. Results show that the model including all parameters predicts settlement discharge with very good accuracy compared to the laboratory model, with a coefficient of determination and root mean square coefficient of 0.949 and 0.058 respectively in the test stage and 0.93 and 0.06 in the test phase estimates. The dyke depth parameter has the greatest effect on leakage flow, while the water head has the least effect among input parameters to the model. Although the adaptive regression multivariate spline model accurately estimates the annual dyke leakage flow rate, it is less accurate than the support vector machine method. |
| format | Article |
| id | doaj-art-7661a8094a9b4108808f7fc0a876dcdc |
| institution | Kabale University |
| issn | 2345-4296 2783-3941 |
| language | English |
| publishDate | 2024-06-01 |
| publisher | K. N. Toosi University of Technology |
| record_format | Article |
| series | Numerical Methods in Civil Engineering |
| spelling | doaj-art-7661a8094a9b4108808f7fc0a876dcdc2024-12-20T08:19:45ZengK. N. Toosi University of TechnologyNumerical Methods in Civil Engineering2345-42962783-39412024-06-0184445010.61186/NMCE.2309.1030200266Estimating Coastal Dyke Leakage Flow Using Support Vector Machine (SVM) and Multivariate Adaptive Regression Spline (MARS) ModelAdel Asakereh0Farhad Choobi1mohammad bagherzadeh2reza mirzaee3Associate Professor, Faculty of Civil Engineering, Semnan University, Semnan, Iran.MSc student, Faculty of Civil Engineering, Semnan University, Semnan, IranPhD student, Department of Civil Engineering, Urmia University, Urmia, IranPhD student, Faculty of Civil Engineering, Semnan University, Semnan, Iran.It is important to check for leakage flow in hydraulic and marine structures during design, as uncontrolled leakage can cause irreparable damage. Soft computing methods can be used to easily model, analyze and control complex systems. This study uses Support Vector Machine (SVM) method to predict leakage discharge of coastal dykes. Five different models are used to achieve this goal, with parameters including the length of the cutoff blanket, dyke depth, and water head considered. The best support vector machine model is checked using a multivariate adaptive regression spline model (MARS) for prediction. Results show that the model including all parameters predicts settlement discharge with very good accuracy compared to the laboratory model, with a coefficient of determination and root mean square coefficient of 0.949 and 0.058 respectively in the test stage and 0.93 and 0.06 in the test phase estimates. The dyke depth parameter has the greatest effect on leakage flow, while the water head has the least effect among input parameters to the model. Although the adaptive regression multivariate spline model accurately estimates the annual dyke leakage flow rate, it is less accurate than the support vector machine method.https://nmce.kntu.ac.ir/article_200266_1202942f55aa0b2a4bc5a978cd5c5b5e.pdfsoft computingseepage dischargecoastal dykesvm methodmars model |
| spellingShingle | Adel Asakereh Farhad Choobi mohammad bagherzadeh reza mirzaee Estimating Coastal Dyke Leakage Flow Using Support Vector Machine (SVM) and Multivariate Adaptive Regression Spline (MARS) Model Numerical Methods in Civil Engineering soft computing seepage discharge coastal dyke svm method mars model |
| title | Estimating Coastal Dyke Leakage Flow Using Support Vector Machine (SVM) and Multivariate Adaptive Regression Spline (MARS) Model |
| title_full | Estimating Coastal Dyke Leakage Flow Using Support Vector Machine (SVM) and Multivariate Adaptive Regression Spline (MARS) Model |
| title_fullStr | Estimating Coastal Dyke Leakage Flow Using Support Vector Machine (SVM) and Multivariate Adaptive Regression Spline (MARS) Model |
| title_full_unstemmed | Estimating Coastal Dyke Leakage Flow Using Support Vector Machine (SVM) and Multivariate Adaptive Regression Spline (MARS) Model |
| title_short | Estimating Coastal Dyke Leakage Flow Using Support Vector Machine (SVM) and Multivariate Adaptive Regression Spline (MARS) Model |
| title_sort | estimating coastal dyke leakage flow using support vector machine svm and multivariate adaptive regression spline mars model |
| topic | soft computing seepage discharge coastal dyke svm method mars model |
| url | https://nmce.kntu.ac.ir/article_200266_1202942f55aa0b2a4bc5a978cd5c5b5e.pdf |
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