Review of nitrogen compounds prediction in water bodies using artificial neural networks and other models
agricultural land; artificial neural network; complexity; concentration (composition); fertilizer application; nitrogen; optimization; prediction; stream; water quality; water treatment
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2023
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my.uniten.dspace-254662023-05-29T16:09:45Z Review of nitrogen compounds prediction in water bodies using artificial neural networks and other models Kumar P. Lai S.H. Wong J.K. Mohd N.S. Kamal M.R. Afan H.A. Ahmed A.N. Sherif M. Sefelnasr A. El-Shafie A. 57206939156 36102664300 57194870148 57192892703 6507669917 56436626600 57214837520 7005414714 6505592467 16068189400 agricultural land; artificial neural network; complexity; concentration (composition); fertilizer application; nitrogen; optimization; prediction; stream; water quality; water treatment 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. � 2020 by the authors. Final 2023-05-29T08:09:45Z 2023-05-29T08:09:45Z 2020 Review 10.3390/su12114359 2-s2.0-85085952776 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85085952776&doi=10.3390%2fsu12114359&partnerID=40&md5=70247d92f3f0e3958a5efbf5411ca71f https://irepository.uniten.edu.my/handle/123456789/25466 12 11 4359 All Open Access, Gold, Green MDPI Scopus |
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| description |
agricultural land; artificial neural network; complexity; concentration (composition); fertilizer application; nitrogen; optimization; prediction; stream; water quality; water treatment |
| author2 |
57206939156 |
| author_facet |
57206939156 Kumar P. Lai S.H. Wong J.K. Mohd N.S. Kamal M.R. Afan H.A. Ahmed A.N. Sherif M. Sefelnasr A. El-Shafie A. |
| format |
Review |
| author |
Kumar P. Lai S.H. Wong J.K. Mohd N.S. Kamal M.R. Afan H.A. Ahmed A.N. Sherif M. Sefelnasr A. El-Shafie A. |
| spellingShingle |
Kumar P. Lai S.H. Wong J.K. Mohd N.S. Kamal M.R. Afan H.A. Ahmed A.N. Sherif M. Sefelnasr A. El-Shafie A. Review of nitrogen compounds prediction in water bodies using artificial neural networks and other models |
| author_sort |
Kumar P. |
| title |
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_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_sort |
review of nitrogen compounds prediction in water bodies using artificial neural networks and other models |
| publisher |
MDPI |
| publishDate |
2023 |
| _version_ |
1806428438198747136 |
| score |
13.222552 |