Enhancement of nitrogen prediction accuracy through a new hybrid model using ant colony optimization and an Elman neural network
Advanced human activities, including modern agricultural practices, are responsible for alteration of natural concentration of nitrogen compounds in rivers. Future prediction of nitrogen compound concentrations (especially nitrate-nitrogen and ammonia-nitrogen) are important for countries where hous...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Taylor and Francis
2021
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| Online Access: | http://psasir.upm.edu.my/id/eprint/97101/ http://psasir.upm.edu.my/id/eprint/97101/1/ABSTRACT.pdf |
| _version_ | 1848862515605274624 |
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| author | Kumar, Pavitra Lai, Sai Hin Mohd, Nuruol Syuhadaa Kamal, Md. Rowshon Ahmed, Ali Najah Sherif, Mohsen Sefelnasr, Ahmed Ahmed El-Shafie, Ahmed Hussein Kamel |
| author_facet | Kumar, Pavitra Lai, Sai Hin Mohd, Nuruol Syuhadaa Kamal, Md. Rowshon Ahmed, Ali Najah Sherif, Mohsen Sefelnasr, Ahmed Ahmed El-Shafie, Ahmed Hussein Kamel |
| author_sort | Kumar, Pavitra |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | Advanced human activities, including modern agricultural practices, are responsible for alteration of natural concentration of nitrogen compounds in rivers. Future prediction of nitrogen compound concentrations (especially nitrate-nitrogen and ammonia-nitrogen) are important for countries where household water is obtained from rivers after treatment. Increased concentrations of nitrogen compounds result in the suspension of household water supplies. Artificial Neural Networks (ANNs) have already been deployed for the prediction of nitrogen compounds in various countries. But standalone ANN have several limitations. However, the limitations of ANNs can be resolved using hybrid models. This study proposes a new ACO-ENN hybrid model developed by integrating Ant Colony Optimization (ACO) with an Elman Neural Network (ENN). The developed ACO-ENN hybrid model was used to improve the prediction results of nitrate-nitrogen and ammonia-nitrogen prediction models. The results of new hybrid models were compared with multilayer ANN models and standalone ENN models. There was a significant improvement in the mean square errors (MSE) (0.196→0.049→0.012, i.e. ANN→ENN→Hybrid), mean absolute errors (MAE) (0.271→0.094→0.069) and Nash–Sutcliffe efficiencies (NSE) (0.7255→0.9321→0.984). The hybrid model had outstanding performance compared with the ANN and ENN models. Hence, the prediction accuracy of nitrate-nitrogen and ammonia-nitrogen has been improved using new ACO-ENN hybrid model. |
| first_indexed | 2025-11-15T13:18:15Z |
| format | Article |
| id | upm-97101 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T13:18:15Z |
| publishDate | 2021 |
| publisher | Taylor and Francis |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-971012022-10-14T03:31:13Z http://psasir.upm.edu.my/id/eprint/97101/ Enhancement of nitrogen prediction accuracy through a new hybrid model using ant colony optimization and an Elman neural network Kumar, Pavitra Lai, Sai Hin Mohd, Nuruol Syuhadaa Kamal, Md. Rowshon Ahmed, Ali Najah Sherif, Mohsen Sefelnasr, Ahmed Ahmed El-Shafie, Ahmed Hussein Kamel Advanced human activities, including modern agricultural practices, are responsible for alteration of natural concentration of nitrogen compounds in rivers. Future prediction of nitrogen compound concentrations (especially nitrate-nitrogen and ammonia-nitrogen) are important for countries where household water is obtained from rivers after treatment. Increased concentrations of nitrogen compounds result in the suspension of household water supplies. Artificial Neural Networks (ANNs) have already been deployed for the prediction of nitrogen compounds in various countries. But standalone ANN have several limitations. However, the limitations of ANNs can be resolved using hybrid models. This study proposes a new ACO-ENN hybrid model developed by integrating Ant Colony Optimization (ACO) with an Elman Neural Network (ENN). The developed ACO-ENN hybrid model was used to improve the prediction results of nitrate-nitrogen and ammonia-nitrogen prediction models. The results of new hybrid models were compared with multilayer ANN models and standalone ENN models. There was a significant improvement in the mean square errors (MSE) (0.196→0.049→0.012, i.e. ANN→ENN→Hybrid), mean absolute errors (MAE) (0.271→0.094→0.069) and Nash–Sutcliffe efficiencies (NSE) (0.7255→0.9321→0.984). The hybrid model had outstanding performance compared with the ANN and ENN models. Hence, the prediction accuracy of nitrate-nitrogen and ammonia-nitrogen has been improved using new ACO-ENN hybrid model. Taylor and Francis 2021 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/97101/1/ABSTRACT.pdf Kumar, Pavitra and Lai, Sai Hin and Mohd, Nuruol Syuhadaa and Kamal, Md. Rowshon and Ahmed, Ali Najah and Sherif, Mohsen and Sefelnasr, Ahmed and Ahmed El-Shafie, Ahmed Hussein Kamel (2021) Enhancement of nitrogen prediction accuracy through a new hybrid model using ant colony optimization and an Elman neural network. Engineering Applications of Computational Fluid Mechanics, 15 (1). 1843 - 1867. ISSN 1994-2060; ESSN: 1997-003X https://www.tandfonline.com/doi/full/10.1080/19942060.2021.1990134 10.1080/19942060.2021.1990134 |
| spellingShingle | Kumar, Pavitra Lai, Sai Hin Mohd, Nuruol Syuhadaa Kamal, Md. Rowshon Ahmed, Ali Najah Sherif, Mohsen Sefelnasr, Ahmed Ahmed El-Shafie, Ahmed Hussein Kamel Enhancement of nitrogen prediction accuracy through a new hybrid model using ant colony optimization and an Elman neural network |
| title | Enhancement of nitrogen prediction accuracy through a new hybrid model using ant colony optimization and an Elman neural network |
| title_full | Enhancement of nitrogen prediction accuracy through a new hybrid model using ant colony optimization and an Elman neural network |
| title_fullStr | Enhancement of nitrogen prediction accuracy through a new hybrid model using ant colony optimization and an Elman neural network |
| title_full_unstemmed | Enhancement of nitrogen prediction accuracy through a new hybrid model using ant colony optimization and an Elman neural network |
| title_short | Enhancement of nitrogen prediction accuracy through a new hybrid model using ant colony optimization and an Elman neural network |
| title_sort | enhancement of nitrogen prediction accuracy through a new hybrid model using ant colony optimization and an elman neural network |
| url | http://psasir.upm.edu.my/id/eprint/97101/ http://psasir.upm.edu.my/id/eprint/97101/ http://psasir.upm.edu.my/id/eprint/97101/ http://psasir.upm.edu.my/id/eprint/97101/1/ABSTRACT.pdf |