Review of nitrogen compounds prediction in water bodies using artificial neural networks and other models
The prediction of nitrogen not only assists in monitoring the nitrogen concentration in streams but also helps in optimizing the usage of fertilizers in agricultural fields. A precise prediction model guarantees the delivering of better-quality water for human use, as the operations of various water...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
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MDPI
2020
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| Online Access: | http://psasir.upm.edu.my/id/eprint/87480/ |
| _version_ | 1848860446515265536 |
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| author | Kumar, Pavitra Sai, Hin Lai Jee, Khai Wong Mohd, Nuruol Syuhadaa Kamal, Md Rowshon Afan, Haitham Abdulmohsin Ahmed, Ali Najah Sherif, Mohsen Sefelnasr, Ahmed El-Shafie, Ahmed |
| author_facet | Kumar, Pavitra Sai, Hin Lai Jee, Khai Wong Mohd, Nuruol Syuhadaa Kamal, Md Rowshon Afan, Haitham Abdulmohsin Ahmed, Ali Najah Sherif, Mohsen Sefelnasr, Ahmed El-Shafie, Ahmed |
| author_sort | Kumar, Pavitra |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | The prediction of nitrogen not only assists in monitoring the nitrogen concentration in streams but also helps in optimizing the usage of fertilizers in agricultural fields. A precise prediction model guarantees the delivering of better-quality water for human use, as the operations of various water treatment plants depend on the concentration of nitrogen in streams. Considering the stochastic nature and the various hydrological variables upon which nitrogen concentration depends, a predictive model should be efficient enough to account for all the complexities of nature in the prediction of nitrogen concentration. For two decades, artificial neural networks (ANNs) and other models (such as autoregressive integrated moving average (ARIMA) model, hybrid model, etc.), used for predicting different complex hydrological parameters, have proved efficient and accurate up to a certain extent. In this review paper, such prediction models, created for predicting nitrogen concentration, are critically analyzed, comparing their accuracy and input variables. Moreover, future research works aiming to predict nitrogen using advanced techniques and more reliable and appropriate input variables are also discussed. |
| first_indexed | 2025-11-15T12:45:22Z |
| format | Article |
| id | upm-87480 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-15T12:45:22Z |
| publishDate | 2020 |
| publisher | MDPI |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-874802023-09-12T02:40:53Z http://psasir.upm.edu.my/id/eprint/87480/ Review of nitrogen compounds prediction in water bodies using artificial neural networks and other models Kumar, Pavitra Sai, Hin Lai Jee, Khai Wong Mohd, Nuruol Syuhadaa Kamal, Md Rowshon Afan, Haitham Abdulmohsin Ahmed, Ali Najah Sherif, Mohsen Sefelnasr, Ahmed El-Shafie, Ahmed The prediction of nitrogen not only assists in monitoring the nitrogen concentration in streams but also helps in optimizing the usage of fertilizers in agricultural fields. A precise prediction model guarantees the delivering of better-quality water for human use, as the operations of various water treatment plants depend on the concentration of nitrogen in streams. Considering the stochastic nature and the various hydrological variables upon which nitrogen concentration depends, a predictive model should be efficient enough to account for all the complexities of nature in the prediction of nitrogen concentration. For two decades, artificial neural networks (ANNs) and other models (such as autoregressive integrated moving average (ARIMA) model, hybrid model, etc.), used for predicting different complex hydrological parameters, have proved efficient and accurate up to a certain extent. In this review paper, such prediction models, created for predicting nitrogen concentration, are critically analyzed, comparing their accuracy and input variables. Moreover, future research works aiming to predict nitrogen using advanced techniques and more reliable and appropriate input variables are also discussed. MDPI 2020-05-26 Article PeerReviewed Kumar, Pavitra and Sai, Hin Lai and Jee, Khai Wong and Mohd, Nuruol Syuhadaa and Kamal, Md Rowshon and Afan, Haitham Abdulmohsin and Ahmed, Ali Najah and Sherif, Mohsen and Sefelnasr, Ahmed and El-Shafie, Ahmed (2020) Review of nitrogen compounds prediction in water bodies using artificial neural networks and other models. Sustainability, 12 (11). art. no. 4359. pp. 1-24. ISSN 2071-1050 https://www.mdpi.com/journal/sustainability 10.3390/su12114359 |
| spellingShingle | Kumar, Pavitra Sai, Hin Lai Jee, Khai Wong Mohd, Nuruol Syuhadaa Kamal, Md Rowshon Afan, Haitham Abdulmohsin Ahmed, Ali Najah Sherif, Mohsen Sefelnasr, Ahmed El-Shafie, Ahmed Review of nitrogen compounds prediction in water bodies using artificial neural networks and other models |
| title | Review of nitrogen compounds prediction in water bodies using artificial neural networks and other models |
| title_full | Review of nitrogen compounds prediction in water bodies using artificial neural networks and other models |
| title_fullStr | Review of nitrogen compounds prediction in water bodies using artificial neural networks and other models |
| title_full_unstemmed | Review of nitrogen compounds prediction in water bodies using artificial neural networks and other models |
| title_short | Review of nitrogen compounds prediction in water bodies using artificial neural networks and other models |
| title_sort | review of nitrogen compounds prediction in water bodies using artificial neural networks and other models |
| url | http://psasir.upm.edu.my/id/eprint/87480/ http://psasir.upm.edu.my/id/eprint/87480/ http://psasir.upm.edu.my/id/eprint/87480/ |