Predicting longitudinal dispersion coefficient using ensemble models and optimized multi-layer perceptron models
Prediction of the longitudinal dispersion coefficient (LDC) is essential for the river and water resources engineering and environmental management. This study proposes ensemble models for predicting LDC based on multilayer perceptron (MULP) methods and optimization algorithms. The honey badger opti...
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| Format: | Article |
| Language: | English |
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Elsevier
2023-12-01
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| Series: | Ain Shams Engineering Journal |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2090447923001120 |
| _version_ | 1797385231161884672 |
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| author | Mahsa Gholami Elham Ghanbari-Adivi Mohammad Ehteram Vijay P. Singh Ali Najah Ahmed Amir Mosavi Ahmed El-Shafie |
| author_facet | Mahsa Gholami Elham Ghanbari-Adivi Mohammad Ehteram Vijay P. Singh Ali Najah Ahmed Amir Mosavi Ahmed El-Shafie |
| author_sort | Mahsa Gholami |
| collection | DOAJ |
| description | Prediction of the longitudinal dispersion coefficient (LDC) is essential for the river and water resources engineering and environmental management. This study proposes ensemble models for predicting LDC based on multilayer perceptron (MULP) methods and optimization algorithms. The honey badger optimization algorithm (HBOA), salp swarm algorithm (SASA), firefly algorithm (FIFA), and particle swarm optimization algorithm (PASOA) are used to adjust the MULP parameters. Then, the outputs of the MULP-HBOA, MULP-SASA, MULP-PASOA, MULP-FIFA, and MULP models were incorporated into an inclusive multiple model (IMM). For IMM at the testing level, the mean absolute error (MEAE) was 15, whereas it was 17, 18, 23, 24, and 25 for the MULP-HBOA, MULP-SASA, MULP-FIFA, MULP-PASOA, and MULP models. The study also modified the structure of MULP models using a goodness factor which decreased the CPU time. Removing redundant neurons reduces CPU time. Thus, the modified ANN model and the suggested IMM model can decrease the computational time and further improve the performance of models. |
| first_indexed | 2024-03-08T21:51:11Z |
| format | Article |
| id | doaj.art-a5b7b2f16c9f4ad1be7b246e46154940 |
| institution | Directory Open Access Journal |
| issn | 2090-4479 |
| language | English |
| last_indexed | 2024-03-08T21:51:11Z |
| publishDate | 2023-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ain Shams Engineering Journal |
| spelling | doaj.art-a5b7b2f16c9f4ad1be7b246e461549402023-12-20T07:34:08ZengElsevierAin Shams Engineering Journal2090-44792023-12-011412102223Predicting longitudinal dispersion coefficient using ensemble models and optimized multi-layer perceptron modelsMahsa Gholami0Elham Ghanbari-Adivi1Mohammad Ehteram2Vijay P. Singh3Ali Najah Ahmed4Amir Mosavi5Ahmed El-Shafie6Department of Civil Engineering, Faculty of Engineering, Bu-Ali Sina University, Hamedan, IranDepartment of Water Science Engineering, Shahrekord University, Shahrekord, IranDepartment of Civil Engineering, Semnan University, Semnan, Iran; Corresponding authors at: Obuda University, Mohammad Ehteram; Semnan University, Iran (A. Mosav).Department of Biological and Agricultural Engineering, Zachry Department of Civil Engineering, Texas A & M University, College Station, TX, 77843-2117, USAInstitute of Energy Infrastructure (IEI) and Department of Civil Engineering, College of Engineering, University Tenaga Nasional (UNITEN), Selangor, MalaysiaJohn von Neumann Faculty of Informatics, Obuda University, 1034 Budapest, Hungary; German Research Center for Artificial Intelligence, Oldenburg, Germany; Corresponding authors at: Obuda University, Mohammad Ehteram; Semnan University, Iran (A. Mosav).Department of Civil Engineering, Faculty of Engineering, University of Malaya (UM), Kuala Lumpur, Malaysia; National Water and Energy Center, United Arab Emirates University, Al Ain, United Arab EmiratesPrediction of the longitudinal dispersion coefficient (LDC) is essential for the river and water resources engineering and environmental management. This study proposes ensemble models for predicting LDC based on multilayer perceptron (MULP) methods and optimization algorithms. The honey badger optimization algorithm (HBOA), salp swarm algorithm (SASA), firefly algorithm (FIFA), and particle swarm optimization algorithm (PASOA) are used to adjust the MULP parameters. Then, the outputs of the MULP-HBOA, MULP-SASA, MULP-PASOA, MULP-FIFA, and MULP models were incorporated into an inclusive multiple model (IMM). For IMM at the testing level, the mean absolute error (MEAE) was 15, whereas it was 17, 18, 23, 24, and 25 for the MULP-HBOA, MULP-SASA, MULP-FIFA, MULP-PASOA, and MULP models. The study also modified the structure of MULP models using a goodness factor which decreased the CPU time. Removing redundant neurons reduces CPU time. Thus, the modified ANN model and the suggested IMM model can decrease the computational time and further improve the performance of models.http://www.sciencedirect.com/science/article/pii/S2090447923001120Longitudinal dispersion coefficientMultilayer perceptronOptimizationArtificial intelligenceMachine learningDeep learning |
| spellingShingle | Mahsa Gholami Elham Ghanbari-Adivi Mohammad Ehteram Vijay P. Singh Ali Najah Ahmed Amir Mosavi Ahmed El-Shafie Predicting longitudinal dispersion coefficient using ensemble models and optimized multi-layer perceptron models Ain Shams Engineering Journal Longitudinal dispersion coefficient Multilayer perceptron Optimization Artificial intelligence Machine learning Deep learning |
| title | Predicting longitudinal dispersion coefficient using ensemble models and optimized multi-layer perceptron models |
| title_full | Predicting longitudinal dispersion coefficient using ensemble models and optimized multi-layer perceptron models |
| title_fullStr | Predicting longitudinal dispersion coefficient using ensemble models and optimized multi-layer perceptron models |
| title_full_unstemmed | Predicting longitudinal dispersion coefficient using ensemble models and optimized multi-layer perceptron models |
| title_short | Predicting longitudinal dispersion coefficient using ensemble models and optimized multi-layer perceptron models |
| title_sort | predicting longitudinal dispersion coefficient using ensemble models and optimized multi layer perceptron models |
| topic | Longitudinal dispersion coefficient Multilayer perceptron Optimization Artificial intelligence Machine learning Deep learning |
| url | http://www.sciencedirect.com/science/article/pii/S2090447923001120 |
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