Comparison of the efficacy of particle swarm optimization and stochastic gradient descent algorithms on multi-layer perceptron model to estimate longitudinal dispersion coefficients in natural streams
Accurate estimation of the longitudinal dispersion coefficient (LDC) is essential for modeling the pollution status in rivers. This research investigates the capabilities of machine-learning methods such as multi-layer perceptron (MLP), multi-layer perceptron trained with particle swarm optimization...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2022-12-01
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| Series: | Engineering Applications of Computational Fluid Mechanics |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/19942060.2022.2141896 |
| _version_ | 1811304789225504768 |
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| author | Tao Hai Hongwei Li Shahab S. Band Sadra Shadkani Saeed Samadianfard Sajjad Hashemi Kwok-Wing Chau Amir Mousavi |
| author_facet | Tao Hai Hongwei Li Shahab S. Band Sadra Shadkani Saeed Samadianfard Sajjad Hashemi Kwok-Wing Chau Amir Mousavi |
| author_sort | Tao Hai |
| collection | DOAJ |
| description | Accurate estimation of the longitudinal dispersion coefficient (LDC) is essential for modeling the pollution status in rivers. This research investigates the capabilities of machine-learning methods such as multi-layer perceptron (MLP), multi-layer perceptron trained with particle swarm optimization (MLP-PSO), multi-layer perceptron trained with Stochastic gradient descent deep learning (MLP-SGD) and different regressions including linear and non-linear regressions (LR and NLR) methods for determining the LDC of pollution in natural rivers and evaluates the accuracy of these methods in comparison with real measured data. Furthermore, the correlation coefficient (CC), root mean squared error (RMSE) and Willmott’s Index (WI) were implemented to evaluate the accuracies of the mentioned methods. Comparison of the results showed the superiority of the MLP-SGD model with CC of 0.923, RMSE of 281.4 and WI of 0.954, which indicates the undeniable accuracy and quality of the deep-learning model that can be used as a powerful model for LDC simulation. Also due to the acceptable performance of the PSO algorithm in the hybridization of the MLP model, the use of PSO algorithms is recommended to train machine-learning techniques for LDC estimation. |
| first_indexed | 2024-04-13T08:13:23Z |
| format | Article |
| id | doaj.art-2c381ca8d63a42469104d3c6126a7254 |
| institution | Directory Open Access Journal |
| issn | 1994-2060 1997-003X |
| language | English |
| last_indexed | 2024-04-13T08:13:23Z |
| publishDate | 2022-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Engineering Applications of Computational Fluid Mechanics |
| spelling | doaj.art-2c381ca8d63a42469104d3c6126a72542022-12-22T02:54:52ZengTaylor & Francis GroupEngineering Applications of Computational Fluid Mechanics1994-20601997-003X2022-12-011612206222010.1080/19942060.2022.2141896Comparison of the efficacy of particle swarm optimization and stochastic gradient descent algorithms on multi-layer perceptron model to estimate longitudinal dispersion coefficients in natural streamsTao Hai0Hongwei Li1Shahab S. Band2Sadra Shadkani3Saeed Samadianfard4Sajjad Hashemi5Kwok-Wing Chau6Amir Mousavi7School of Computer and Information, Qiannan Normal University for Nationalities, Duyun, Guizhou, People’s Republic of ChinaSchool of Information Engineering, Yulin university, Yulin, People’s Republic of ChinaFuture Technology Research Center, College of Future, National Yunlin University of Science and Technology, Yunlin, TaiwanDepartment of Water Engineering, Faculty of Agriculture, University of Tabriz, Tabriz, IranDepartment of Water Engineering, Faculty of Agriculture, University of Tabriz, Tabriz, IranDepartment of Water Engineering, Faculty of Agriculture, University of Tabriz, Tabriz, IranDepartment of Civil and Environmental Engineering, Hong Kong Polytechnic University, Hong Kong, People’s Republic of ChinaObuda University, Budapest, HungaryAccurate estimation of the longitudinal dispersion coefficient (LDC) is essential for modeling the pollution status in rivers. This research investigates the capabilities of machine-learning methods such as multi-layer perceptron (MLP), multi-layer perceptron trained with particle swarm optimization (MLP-PSO), multi-layer perceptron trained with Stochastic gradient descent deep learning (MLP-SGD) and different regressions including linear and non-linear regressions (LR and NLR) methods for determining the LDC of pollution in natural rivers and evaluates the accuracy of these methods in comparison with real measured data. Furthermore, the correlation coefficient (CC), root mean squared error (RMSE) and Willmott’s Index (WI) were implemented to evaluate the accuracies of the mentioned methods. Comparison of the results showed the superiority of the MLP-SGD model with CC of 0.923, RMSE of 281.4 and WI of 0.954, which indicates the undeniable accuracy and quality of the deep-learning model that can be used as a powerful model for LDC simulation. Also due to the acceptable performance of the PSO algorithm in the hybridization of the MLP model, the use of PSO algorithms is recommended to train machine-learning techniques for LDC estimation.https://www.tandfonline.com/doi/10.1080/19942060.2022.2141896Longitudinal dispersion coefficientmulti-layer perceptronparticle swarm optimizationstochastic gradient descentdeep learningstatistical evaluation |
| spellingShingle | Tao Hai Hongwei Li Shahab S. Band Sadra Shadkani Saeed Samadianfard Sajjad Hashemi Kwok-Wing Chau Amir Mousavi Comparison of the efficacy of particle swarm optimization and stochastic gradient descent algorithms on multi-layer perceptron model to estimate longitudinal dispersion coefficients in natural streams Engineering Applications of Computational Fluid Mechanics Longitudinal dispersion coefficient multi-layer perceptron particle swarm optimization stochastic gradient descent deep learning statistical evaluation |
| title | Comparison of the efficacy of particle swarm optimization and stochastic gradient descent algorithms on multi-layer perceptron model to estimate longitudinal dispersion coefficients in natural streams |
| title_full | Comparison of the efficacy of particle swarm optimization and stochastic gradient descent algorithms on multi-layer perceptron model to estimate longitudinal dispersion coefficients in natural streams |
| title_fullStr | Comparison of the efficacy of particle swarm optimization and stochastic gradient descent algorithms on multi-layer perceptron model to estimate longitudinal dispersion coefficients in natural streams |
| title_full_unstemmed | Comparison of the efficacy of particle swarm optimization and stochastic gradient descent algorithms on multi-layer perceptron model to estimate longitudinal dispersion coefficients in natural streams |
| title_short | Comparison of the efficacy of particle swarm optimization and stochastic gradient descent algorithms on multi-layer perceptron model to estimate longitudinal dispersion coefficients in natural streams |
| title_sort | comparison of the efficacy of particle swarm optimization and stochastic gradient descent algorithms on multi layer perceptron model to estimate longitudinal dispersion coefficients in natural streams |
| topic | Longitudinal dispersion coefficient multi-layer perceptron particle swarm optimization stochastic gradient descent deep learning statistical evaluation |
| url | https://www.tandfonline.com/doi/10.1080/19942060.2022.2141896 |
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