Rotary gate discharge determination for inclusive data from free to submerged flow conditions using ENN, ENN–GA, and SVM–SA
This study aims at evaluating the performance of the Elman Neural Network (ENN), Elman Neural Network-Genetic Algorithm (ENN–GA), and Support Vector Machine-simulated annealing (SVM–SA) in determining the discharge of a newly proposed rotary gate for the inclusive data range from free flow to highly...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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IWA Publishing
2023-07-01
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| Series: | Journal of Hydroinformatics |
| Subjects: | |
| Online Access: | http://jhydro.iwaponline.com/content/25/4/1312 |
| _version_ | 1797745600056262656 |
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| author | Ava Marashi Salah Kouchakzadeh Hojjat Allah Yonesi |
| author_facet | Ava Marashi Salah Kouchakzadeh Hojjat Allah Yonesi |
| author_sort | Ava Marashi |
| collection | DOAJ |
| description | This study aims at evaluating the performance of the Elman Neural Network (ENN), Elman Neural Network-Genetic Algorithm (ENN–GA), and Support Vector Machine-simulated annealing (SVM–SA) in determining the discharge of a newly proposed rotary gate for the inclusive data range from free flow to highly submerged conditions. For individual free and submerged flows, the models performed as well as that of the traditional relationships. However, the superiority of the intelligent models comes when dealing with the inclusive data set of both flow conditions, where no deterministic approach is available for discharge evaluation prior to specifying the threshold condition. In such complex flow conditions, the ENN–GA hybrid model with a proper structure determined the discharge with rather a high accuracy, i.e., SE of 6.12%. Also, in defining the threshold state, the ENN and ENN–GA models achieved superior results compared to the currently available relationship, i.e., it accurately recognized the threshold condition in almost 100% of the cases while the traditional relationship results were limited to 93% of the cases. Such accuracy of the employed model in assessing the discharge of the structure and its high ability in recognizing the flow state could be of great advantage for irrigation network structure automation.
HIGHLIGHTS
Application of the ENN, ENN-GA, SVM-SA to determine the flow parameters of the new rotary gate in free, submerged flow conditions, and more importantly the inclusive data from free to highly submerged state which was examined for the first time in this research.;
To recognize the complex threshold flow between free and submerged conditions and the proposed Artificial Intelligence models with more than 98% accuracy which is crucial in field conditions and for gate automation.; |
| first_indexed | 2024-03-12T15:25:39Z |
| format | Article |
| id | doaj.art-51318894ffd14a568ab133ee247dc498 |
| institution | Directory Open Access Journal |
| issn | 1464-7141 1465-1734 |
| language | English |
| last_indexed | 2024-03-12T15:25:39Z |
| publishDate | 2023-07-01 |
| publisher | IWA Publishing |
| record_format | Article |
| series | Journal of Hydroinformatics |
| spelling | doaj.art-51318894ffd14a568ab133ee247dc4982023-08-10T13:22:59ZengIWA PublishingJournal of Hydroinformatics1464-71411465-17342023-07-012541312132810.2166/hydro.2023.202202Rotary gate discharge determination for inclusive data from free to submerged flow conditions using ENN, ENN–GA, and SVM–SAAva Marashi0Salah Kouchakzadeh1Hojjat Allah Yonesi2 Ph.D. Graduate, Department of Water Engineering, College of Agriculture, Lorestan University, Iran Irrigation and Reclamation Eng. Dept., University of Tehran, P.O. Box 31587-4111, Karaj 31587-77871, Iran Department of Water Engineering, College of Agriculture, Lorestan University, Lorestan, Iran This study aims at evaluating the performance of the Elman Neural Network (ENN), Elman Neural Network-Genetic Algorithm (ENN–GA), and Support Vector Machine-simulated annealing (SVM–SA) in determining the discharge of a newly proposed rotary gate for the inclusive data range from free flow to highly submerged conditions. For individual free and submerged flows, the models performed as well as that of the traditional relationships. However, the superiority of the intelligent models comes when dealing with the inclusive data set of both flow conditions, where no deterministic approach is available for discharge evaluation prior to specifying the threshold condition. In such complex flow conditions, the ENN–GA hybrid model with a proper structure determined the discharge with rather a high accuracy, i.e., SE of 6.12%. Also, in defining the threshold state, the ENN and ENN–GA models achieved superior results compared to the currently available relationship, i.e., it accurately recognized the threshold condition in almost 100% of the cases while the traditional relationship results were limited to 93% of the cases. Such accuracy of the employed model in assessing the discharge of the structure and its high ability in recognizing the flow state could be of great advantage for irrigation network structure automation. HIGHLIGHTS Application of the ENN, ENN-GA, SVM-SA to determine the flow parameters of the new rotary gate in free, submerged flow conditions, and more importantly the inclusive data from free to highly submerged state which was examined for the first time in this research.; To recognize the complex threshold flow between free and submerged conditions and the proposed Artificial Intelligence models with more than 98% accuracy which is crucial in field conditions and for gate automation.;http://jhydro.iwaponline.com/content/25/4/1312ennenn–gaflow conditionrotary gatesemicircular canalsvm–sa |
| spellingShingle | Ava Marashi Salah Kouchakzadeh Hojjat Allah Yonesi Rotary gate discharge determination for inclusive data from free to submerged flow conditions using ENN, ENN–GA, and SVM–SA Journal of Hydroinformatics enn enn–ga flow condition rotary gate semicircular canal svm–sa |
| title | Rotary gate discharge determination for inclusive data from free to submerged flow conditions using ENN, ENN–GA, and SVM–SA |
| title_full | Rotary gate discharge determination for inclusive data from free to submerged flow conditions using ENN, ENN–GA, and SVM–SA |
| title_fullStr | Rotary gate discharge determination for inclusive data from free to submerged flow conditions using ENN, ENN–GA, and SVM–SA |
| title_full_unstemmed | Rotary gate discharge determination for inclusive data from free to submerged flow conditions using ENN, ENN–GA, and SVM–SA |
| title_short | Rotary gate discharge determination for inclusive data from free to submerged flow conditions using ENN, ENN–GA, and SVM–SA |
| title_sort | rotary gate discharge determination for inclusive data from free to submerged flow conditions using enn enn ga and svm sa |
| topic | enn enn–ga flow condition rotary gate semicircular canal svm–sa |
| url | http://jhydro.iwaponline.com/content/25/4/1312 |
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