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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Ain Shams University
2024
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| author | Gholami M. Ghanbari-Adivi E. Ehteram M. Singh V.P. Najah Ahmed A. Mosavi A. El-Shafie A. |
| author2 | 56973673400 |
| author_facet | 56973673400 Gholami M. Ghanbari-Adivi E. Ehteram M. Singh V.P. Najah Ahmed A. Mosavi A. El-Shafie A. |
| author_sort | Gholami M. |
| building | UNITEN Library |
| collection | Institutional Repository |
| content_provider | Universiti Tenaga Nasional |
| content_source | UNITEN Institutional Repository |
| continent | Asia |
| country | Malaysia |
| 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. � 2023 THE AUTHORS |
| format | Article |
| id | my.uniten.dspace-33906 |
| institution | Universiti Tenaga Nasional |
| publishDate | 2024 |
| publisher | Ain Shams University |
| record_format | dspace |
| spelling | my.uniten.dspace-339062024-10-14T11:17:25Z Predicting longitudinal dispersion coefficient using ensemble models and optimized multi-layer perceptron models Gholami M. Ghanbari-Adivi E. Ehteram M. Singh V.P. Najah Ahmed A. Mosavi A. El-Shafie A. 56973673400 57222383988 57113510800 57211219633 58136810800 57191408081 16068189400 Artificial intelligence Big data Deep learning Longitudinal dispersion coefficient Machine learning Multilayer perceptron Optimization Big data Deep learning Dispersions Environmental management Forecasting Learning systems Multilayer neural networks Multilayers Water resources Deep learning Firefly algorithms Longitudinal dispersion coefficient Machine-learning Multilayers perceptrons Optimisations Optimization algorithms Particle swarm optimization algorithm Salp swarms Swarm algorithms Particle swarm optimization (PSO) 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. � 2023 THE AUTHORS Final 2024-10-14T03:17:25Z 2024-10-14T03:17:25Z 2023 Article 10.1016/j.asej.2023.102223 2-s2.0-85149766266 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85149766266&doi=10.1016%2fj.asej.2023.102223&partnerID=40&md5=aeede0035fc62f36fd7c491aaa9f15e7 https://irepository.uniten.edu.my/handle/123456789/33906 14 12 102223 All Open Access Gold Open Access Ain Shams University Scopus |
| spellingShingle | Artificial intelligence Big data Deep learning Longitudinal dispersion coefficient Machine learning Multilayer perceptron Optimization Big data Deep learning Dispersions Environmental management Forecasting Learning systems Multilayer neural networks Multilayers Water resources Deep learning Firefly algorithms Longitudinal dispersion coefficient Machine-learning Multilayers perceptrons Optimisations Optimization algorithms Particle swarm optimization algorithm Salp swarms Swarm algorithms Particle swarm optimization (PSO) Gholami M. Ghanbari-Adivi E. Ehteram M. Singh V.P. Najah Ahmed A. Mosavi A. El-Shafie A. Predicting longitudinal dispersion coefficient using ensemble models and optimized multi-layer perceptron models |
| 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 | Artificial intelligence Big data Deep learning Longitudinal dispersion coefficient Machine learning Multilayer perceptron Optimization Big data Deep learning Dispersions Environmental management Forecasting Learning systems Multilayer neural networks Multilayers Water resources Deep learning Firefly algorithms Longitudinal dispersion coefficient Machine-learning Multilayers perceptrons Optimisations Optimization algorithms Particle swarm optimization algorithm Salp swarms Swarm algorithms Particle swarm optimization (PSO) |
| url_provider | http://dspace.uniten.edu.my/ |