Development of Neural Network Model Based on Conjugate Gradient and Resilient Back-Propagation Training Function for Estimation of Longitudinal Dispersion Coefficient in Rivers
Determining the longitudinal dispersion coefficient (LDC) for Advection-Diffusion equation is the first step in water quality modeling for one-dimensional water bodies such as rivers. In this research, an artificial neural network (ANN) model has been developed based on the standard numerical optimi...
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| Format: | Article |
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Marvdasht Branch, Islamic Azad University
2019-08-01
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| Series: | مهندسی منابع آب |
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| Online Access: | https://wej.marvdasht.iau.ir/article_3594_1f301376129c53a9841da9b46bd5084a.pdf |
| _version_ | 1797359732464287744 |
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| author | Roohollah Noori Behzad Ghiasi عبدالرضا کرباسی امین سارنگ |
| author_facet | Roohollah Noori Behzad Ghiasi عبدالرضا کرباسی امین سارنگ |
| author_sort | Roohollah Noori |
| collection | DOAJ |
| description | Determining the longitudinal dispersion coefficient (LDC) for Advection-Diffusion equation is the first step in water quality modeling for one-dimensional water bodies such as rivers. In this research, an artificial neural network (ANN) model has been developed based on the standard numerical optimization algorithms and heuristic techniques to determine the LDC. In this regard, conjugate gradient (CG) training functions including Fletcher-Reeves, Polak-Ribiére, Powell-Beale and scaled conjugate gradient functions from the standard numerical optimization algorithms category and resilient back-propagation (Trainrp) training function from the heuristic algorithms, have been applied to optimizing ANN parameters. Then, the best model has been selected for each of the training functions according to indices that are used to evaluate results. Among the selected models, the ANN model with the Trainrp training function has been selected as the best model to predict the LDC due to DDR statistic. Finally, a comparison has been undertaken between the selected model and other suggested artificial intelligent methods by the researchers. According to the implemented comparisons, the Trainrp function acquired the best performance. |
| first_indexed | 2024-03-08T15:27:58Z |
| format | Article |
| id | doaj.art-efafd4250d9f404f94003587fdcf32f7 |
| institution | Directory Open Access Journal |
| issn | 2008-6377 2423-7191 |
| language | fas |
| last_indexed | 2024-03-08T15:27:58Z |
| publishDate | 2019-08-01 |
| publisher | Marvdasht Branch, Islamic Azad University |
| record_format | Article |
| series | مهندسی منابع آب |
| spelling | doaj.art-efafd4250d9f404f94003587fdcf32f72024-01-10T08:10:51ZfasMarvdasht Branch, Islamic Azad Universityمهندسی منابع آب2008-63772423-71912019-08-01124163783594Development of Neural Network Model Based on Conjugate Gradient and Resilient Back-Propagation Training Function for Estimation of Longitudinal Dispersion Coefficient in RiversRoohollah Noori0Behzad Ghiasi1عبدالرضا کرباسی2امین سارنگ3Graduate Faculty of Environment, University of Tehran, No. 23, Ghods St., Enghelab Ave., Tehran, Iran, P.O.BOX: 14155-6135دانشگاه تهراندانشگاه تهراندانشگاه تهرانDetermining the longitudinal dispersion coefficient (LDC) for Advection-Diffusion equation is the first step in water quality modeling for one-dimensional water bodies such as rivers. In this research, an artificial neural network (ANN) model has been developed based on the standard numerical optimization algorithms and heuristic techniques to determine the LDC. In this regard, conjugate gradient (CG) training functions including Fletcher-Reeves, Polak-Ribiére, Powell-Beale and scaled conjugate gradient functions from the standard numerical optimization algorithms category and resilient back-propagation (Trainrp) training function from the heuristic algorithms, have been applied to optimizing ANN parameters. Then, the best model has been selected for each of the training functions according to indices that are used to evaluate results. Among the selected models, the ANN model with the Trainrp training function has been selected as the best model to predict the LDC due to DDR statistic. Finally, a comparison has been undertaken between the selected model and other suggested artificial intelligent methods by the researchers. According to the implemented comparisons, the Trainrp function acquired the best performance.https://wej.marvdasht.iau.ir/article_3594_1f301376129c53a9841da9b46bd5084a.pdflongitudinal dispersion coefficientwater pollutantintelligence modelstraining algorithm |
| spellingShingle | Roohollah Noori Behzad Ghiasi عبدالرضا کرباسی امین سارنگ Development of Neural Network Model Based on Conjugate Gradient and Resilient Back-Propagation Training Function for Estimation of Longitudinal Dispersion Coefficient in Rivers مهندسی منابع آب longitudinal dispersion coefficient water pollutant intelligence models training algorithm |
| title | Development of Neural Network Model Based on Conjugate Gradient and Resilient Back-Propagation Training Function for Estimation of Longitudinal Dispersion Coefficient in Rivers |
| title_full | Development of Neural Network Model Based on Conjugate Gradient and Resilient Back-Propagation Training Function for Estimation of Longitudinal Dispersion Coefficient in Rivers |
| title_fullStr | Development of Neural Network Model Based on Conjugate Gradient and Resilient Back-Propagation Training Function for Estimation of Longitudinal Dispersion Coefficient in Rivers |
| title_full_unstemmed | Development of Neural Network Model Based on Conjugate Gradient and Resilient Back-Propagation Training Function for Estimation of Longitudinal Dispersion Coefficient in Rivers |
| title_short | Development of Neural Network Model Based on Conjugate Gradient and Resilient Back-Propagation Training Function for Estimation of Longitudinal Dispersion Coefficient in Rivers |
| title_sort | development of neural network model based on conjugate gradient and resilient back propagation training function for estimation of longitudinal dispersion coefficient in rivers |
| topic | longitudinal dispersion coefficient water pollutant intelligence models training algorithm |
| url | https://wej.marvdasht.iau.ir/article_3594_1f301376129c53a9841da9b46bd5084a.pdf |
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