Modeling the Behavior of Density Current with Machine Learning Algorithms
AbstractIntroduction: Density current is one of the factors influencing the transfer of sediments to reservoirs of dams. One of the practical methods to control sediments is to build an obstacle in the path of these currents.Methods: In this laboratory research, the behavior of the Density current u...
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Marvdasht Branch, Islamic Azad University
2022-10-01
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| Series: | مهندسی منابع آب |
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| Online Access: | https://wej.marvdasht.iau.ir/article_5691_172e67793b19d54bf21d33e99a9b1837.pdf |
| _version_ | 1827385530837368832 |
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| author | Mehdi Derakhshannia Mehdi Ghomeshi Seyed Saeid Eslamian Seyed Mahmood Kashefipour |
| author_facet | Mehdi Derakhshannia Mehdi Ghomeshi Seyed Saeid Eslamian Seyed Mahmood Kashefipour |
| author_sort | Mehdi Derakhshannia |
| collection | DOAJ |
| description | AbstractIntroduction: Density current is one of the factors influencing the transfer of sediments to reservoirs of dams. One of the practical methods to control sediments is to build an obstacle in the path of these currents.Methods: In this laboratory research, the behavior of the Density current under the effect of cylindrical obstacles made of wood with a diameter of 1.5 cm and a height of 30 cm (more than the height of the body of the Density current) was evaluated. Therefore, by considering variables such as floor slope, concentration and discharge, the values of the density current head were determined. Machine learning algorithms such as adaptive neural fuzzy inference system and artificial neural network were used to model the results.Findings: Based on the results, the density salt flow head was modeled using machine learning algorithms such as adaptive fuzzy neural inference system and artificial neural network and the performance of these two methods were compared. The results showed that machine learning algorithms are useful in modeling the density salt flow head. And the regression of the adaptive neural fuzzy inference system for the training and test data was 0.99 and the regression of the artificial neural network was 0.94 and 0.91, respectively.Conclusion: By comparing the two methods, it was found that the adaptive neural-fuzzy inference system is more effective in modeling the percent reduction of the head of Density current than the feed-forward artificial neural network method. |
| first_indexed | 2024-03-08T15:27:45Z |
| format | Article |
| id | doaj.art-b7f166b299cb4283a8280ea01841b678 |
| institution | Directory Open Access Journal |
| issn | 2008-6377 2423-7191 |
| language | fas |
| last_indexed | 2024-03-08T15:27:45Z |
| publishDate | 2022-10-01 |
| publisher | Marvdasht Branch, Islamic Azad University |
| record_format | Article |
| series | مهندسی منابع آب |
| spelling | doaj.art-b7f166b299cb4283a8280ea01841b6782024-01-10T08:11:51ZfasMarvdasht Branch, Islamic Azad Universityمهندسی منابع آب2008-63772423-71912022-10-011554294210.30495/wej.2021.27117.22905691Modeling the Behavior of Density Current with Machine Learning AlgorithmsMehdi Derakhshannia0Mehdi Ghomeshi1Seyed Saeid Eslamian2Seyed Mahmood Kashefipour3Ph.D. Candidate, Department of Civil Engineering, Najafabad Branch, Islamic Azad University, Najafabad, IranProfessor, Department of Water Structures, Faculty of Water and Environmental Engineering, Shahid Chamran University of Ahvaz, Ahvaz, IranProfessor, Department of Civil Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran. and Department of Water Engineering, College of Agriculture, Isfahan University of Technology, Isfahan, IranProfessor, Department of Water Structures, Faculty of Water and Environmental Engineering, Shahid Chamran University of Ahvaz, Ahvaz, IranAbstractIntroduction: Density current is one of the factors influencing the transfer of sediments to reservoirs of dams. One of the practical methods to control sediments is to build an obstacle in the path of these currents.Methods: In this laboratory research, the behavior of the Density current under the effect of cylindrical obstacles made of wood with a diameter of 1.5 cm and a height of 30 cm (more than the height of the body of the Density current) was evaluated. Therefore, by considering variables such as floor slope, concentration and discharge, the values of the density current head were determined. Machine learning algorithms such as adaptive neural fuzzy inference system and artificial neural network were used to model the results.Findings: Based on the results, the density salt flow head was modeled using machine learning algorithms such as adaptive fuzzy neural inference system and artificial neural network and the performance of these two methods were compared. The results showed that machine learning algorithms are useful in modeling the density salt flow head. And the regression of the adaptive neural fuzzy inference system for the training and test data was 0.99 and the regression of the artificial neural network was 0.94 and 0.91, respectively.Conclusion: By comparing the two methods, it was found that the adaptive neural-fuzzy inference system is more effective in modeling the percent reduction of the head of Density current than the feed-forward artificial neural network method.https://wej.marvdasht.iau.ir/article_5691_172e67793b19d54bf21d33e99a9b1837.pdfdensity currenthead reduction percentagesedimentationadaptive neural-fuzzy inference systemfeed-forward artificial neural network |
| spellingShingle | Mehdi Derakhshannia Mehdi Ghomeshi Seyed Saeid Eslamian Seyed Mahmood Kashefipour Modeling the Behavior of Density Current with Machine Learning Algorithms مهندسی منابع آب density current head reduction percentage sedimentation adaptive neural-fuzzy inference system feed-forward artificial neural network |
| title | Modeling the Behavior of Density Current with Machine Learning Algorithms |
| title_full | Modeling the Behavior of Density Current with Machine Learning Algorithms |
| title_fullStr | Modeling the Behavior of Density Current with Machine Learning Algorithms |
| title_full_unstemmed | Modeling the Behavior of Density Current with Machine Learning Algorithms |
| title_short | Modeling the Behavior of Density Current with Machine Learning Algorithms |
| title_sort | modeling the behavior of density current with machine learning algorithms |
| topic | density current head reduction percentage sedimentation adaptive neural-fuzzy inference system feed-forward artificial neural network |
| url | https://wej.marvdasht.iau.ir/article_5691_172e67793b19d54bf21d33e99a9b1837.pdf |
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