Simulation of discharge coefficient of side weirs placed on convergent canals using modern self-adaptive extreme learning machine
Abstract Side weirs are broadly used in irrigation channels, drainage systems and sewage disposal canals for controlling and adjusting the flow in main channels. In this study, a new artificial intelligence model entitled “self-adaptive extreme learning machine” (SAELM) is developed for simulating t...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2020-01-01
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| Series: | Applied Water Science |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s13201-019-1136-0 |
| _version_ | 1818964321544175616 |
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| author | Reza Gharib Majeid Heydari Saeid Kardar Saeid Shabanlou |
| author_facet | Reza Gharib Majeid Heydari Saeid Kardar Saeid Shabanlou |
| author_sort | Reza Gharib |
| collection | DOAJ |
| description | Abstract Side weirs are broadly used in irrigation channels, drainage systems and sewage disposal canals for controlling and adjusting the flow in main channels. In this study, a new artificial intelligence model entitled “self-adaptive extreme learning machine” (SAELM) is developed for simulating the discharge coefficient of side weirs located upon rectangular channels. Also, the Monte Carlo simulations are implemented for assessing the abilities of the numerical models. It should be noted that the k-fold cross-validation approach is used for validating the results obtained from the numerical models. Based on the parameters affecting the discharge coefficient, six artificial intelligence models are defined. The examination of the numerical models exhibits that such models simulate the discharge coefficient valued with acceptable accuracy. For instance, mean absolute error and root mean square error for the superior model are computed 0.022 and 0.027, respectively. The best SAELM model predicts the discharge coefficient values in terms of Froude number (F d), ratio of the side weir height to the downstream depth (w/h d), ratio of the channel width at downstream to the downstream depth (b d/h d) and ratio of the side weir length to the downstream depth (L/h d). Based on the sensitivity analysis results, the Froude number of the side weir downstream is identified as the most influencing input parameter. Lastly, a matrix is presented to estimate the discharge coefficient of side weirs on convergent channels. |
| first_indexed | 2024-12-20T12:59:15Z |
| format | Article |
| id | doaj.art-40812a032fb44805b6559d1836af643b |
| institution | Directory Open Access Journal |
| issn | 2190-5487 2190-5495 |
| language | English |
| last_indexed | 2024-12-20T12:59:15Z |
| publishDate | 2020-01-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Applied Water Science |
| spelling | doaj.art-40812a032fb44805b6559d1836af643b2022-12-21T19:39:57ZengSpringerOpenApplied Water Science2190-54872190-54952020-01-0110111110.1007/s13201-019-1136-0Simulation of discharge coefficient of side weirs placed on convergent canals using modern self-adaptive extreme learning machineReza Gharib0Majeid Heydari1Saeid Kardar2Saeid Shabanlou3Department of Water Engineering, Faculty of Agriculture, Bu-Ali Sina UniversityDepartment of Water Engineering, Faculty of Agriculture, Bu-Ali Sina UniversityDepartment of Architecture, Science and Research Branch, Islamic Azad UniversityDepartment of Water Engineering, Kermanshah Branch, Islamic Azad UniversityAbstract Side weirs are broadly used in irrigation channels, drainage systems and sewage disposal canals for controlling and adjusting the flow in main channels. In this study, a new artificial intelligence model entitled “self-adaptive extreme learning machine” (SAELM) is developed for simulating the discharge coefficient of side weirs located upon rectangular channels. Also, the Monte Carlo simulations are implemented for assessing the abilities of the numerical models. It should be noted that the k-fold cross-validation approach is used for validating the results obtained from the numerical models. Based on the parameters affecting the discharge coefficient, six artificial intelligence models are defined. The examination of the numerical models exhibits that such models simulate the discharge coefficient valued with acceptable accuracy. For instance, mean absolute error and root mean square error for the superior model are computed 0.022 and 0.027, respectively. The best SAELM model predicts the discharge coefficient values in terms of Froude number (F d), ratio of the side weir height to the downstream depth (w/h d), ratio of the channel width at downstream to the downstream depth (b d/h d) and ratio of the side weir length to the downstream depth (L/h d). Based on the sensitivity analysis results, the Froude number of the side weir downstream is identified as the most influencing input parameter. Lastly, a matrix is presented to estimate the discharge coefficient of side weirs on convergent channels.https://doi.org/10.1007/s13201-019-1136-0Side weirDischarge coefficientConvergent channelSelf-adaptive extreme learning machineSensitivity analysis |
| spellingShingle | Reza Gharib Majeid Heydari Saeid Kardar Saeid Shabanlou Simulation of discharge coefficient of side weirs placed on convergent canals using modern self-adaptive extreme learning machine Applied Water Science Side weir Discharge coefficient Convergent channel Self-adaptive extreme learning machine Sensitivity analysis |
| title | Simulation of discharge coefficient of side weirs placed on convergent canals using modern self-adaptive extreme learning machine |
| title_full | Simulation of discharge coefficient of side weirs placed on convergent canals using modern self-adaptive extreme learning machine |
| title_fullStr | Simulation of discharge coefficient of side weirs placed on convergent canals using modern self-adaptive extreme learning machine |
| title_full_unstemmed | Simulation of discharge coefficient of side weirs placed on convergent canals using modern self-adaptive extreme learning machine |
| title_short | Simulation of discharge coefficient of side weirs placed on convergent canals using modern self-adaptive extreme learning machine |
| title_sort | simulation of discharge coefficient of side weirs placed on convergent canals using modern self adaptive extreme learning machine |
| topic | Side weir Discharge coefficient Convergent channel Self-adaptive extreme learning machine Sensitivity analysis |
| url | https://doi.org/10.1007/s13201-019-1136-0 |
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