Optimised neural network model for river-nitrogen prediction utilizing a new training approach
ammonia; nitrogen; nitrogen; Article; artificial neural network; concentration (parameter); controlled study; generalized regression neural network; geography; hydrology; Malaysia; measurement accuracy; multilayer neural network; prediction; radial basis function neural network; stream (river); agri...
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my.uniten.dspace-252602023-05-29T16:07:39Z Optimised neural network model for river-nitrogen prediction utilizing a new training approach Kumar P. Lai S.H. Mohd N.S. Kamal M.R. Afan H.A. Ahmed A.N. Sherif M. Sefelnasr A. El-Shafie A. 57206939156 36102664300 57192892703 6507669917 56436626600 57214837520 7005414714 6505592467 16068189400 ammonia; nitrogen; nitrogen; Article; artificial neural network; concentration (parameter); controlled study; generalized regression neural network; geography; hydrology; Malaysia; measurement accuracy; multilayer neural network; prediction; radial basis function neural network; stream (river); agriculture; chemistry; environmental monitoring; procedures; river; water pollutant; water quality; Agriculture; Environmental Monitoring; Hydrology; Malaysia; Neural Networks, Computer; Nitrogen; Rivers; Water Pollutants, Chemical; Water Quality In the past few decades, there has been a rapid growth in the concentration of nitrogenous compounds such as nitrate-nitrogen and ammonia-nitrogen in rivers, primarily due to increasing agricultural and industrial activities. These nitrogenous compounds are mainly responsible for eutrophication when present in river water, and for �blue baby syndrome� when present in drinking water. High concentrations of these compounds in rivers may eventually lead to the closure of treatment plants. This study presents a training and a selection approach to develop an optimum artificial neural network model for predicting monthly average nitrate-N and monthly average ammonia-N. Several studies have predicted these compounds, but most of the proposed procedures do not involve testing various model architectures in order to achieve the optimum predicting model. Additionally, none of the models have been trained for hydrological conditions such as the case of Malaysia. This study presents models trained on the hydrological data from 1981 to 2017 for the Langat River in Selangor, Malaysia. The model architectures used for training are General Regression Neural Network (GRNN), Multilayer Neural Network and Radial Basis Function Neural Network (RBFNN). These models were trained for various combinations of internal parameters, input variables and model architectures. Post-training, the optimum performing model was selected based on the regression and error values and plot of predicted versus observed values. Optimum models provide promising results with a minimum overall regression value of 0.92. Copyright: � 2020 Kumar et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Final 2023-05-29T08:07:39Z 2023-05-29T08:07:39Z 2020 Article 10.1371/journal.pone.0239509 2-s2.0-85092050972 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85092050972&doi=10.1371%2fjournal.pone.0239509&partnerID=40&md5=bcd0108855dfe7e7fc594e321690f040 https://irepository.uniten.edu.my/handle/123456789/25260 15 9-Sep e0239509 All Open Access, Gold, Green Public Library of Science Scopus |
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ammonia; nitrogen; nitrogen; Article; artificial neural network; concentration (parameter); controlled study; generalized regression neural network; geography; hydrology; Malaysia; measurement accuracy; multilayer neural network; prediction; radial basis function neural network; stream (river); agriculture; chemistry; environmental monitoring; procedures; river; water pollutant; water quality; Agriculture; Environmental Monitoring; Hydrology; Malaysia; Neural Networks, Computer; Nitrogen; Rivers; Water Pollutants, Chemical; Water Quality |
| author2 |
57206939156 |
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57206939156 Kumar P. Lai S.H. Mohd N.S. Kamal M.R. Afan H.A. Ahmed A.N. Sherif M. Sefelnasr A. El-Shafie A. |
| format |
Article |
| author |
Kumar P. Lai S.H. 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. Mohd N.S. Kamal M.R. Afan H.A. Ahmed A.N. Sherif M. Sefelnasr A. El-Shafie A. Optimised neural network model for river-nitrogen prediction utilizing a new training approach |
| author_sort |
Kumar P. |
| title |
Optimised neural network model for river-nitrogen prediction utilizing a new training approach |
| title_short |
Optimised neural network model for river-nitrogen prediction utilizing a new training approach |
| title_full |
Optimised neural network model for river-nitrogen prediction utilizing a new training approach |
| title_fullStr |
Optimised neural network model for river-nitrogen prediction utilizing a new training approach |
| title_full_unstemmed |
Optimised neural network model for river-nitrogen prediction utilizing a new training approach |
| title_sort |
optimised neural network model for river-nitrogen prediction utilizing a new training approach |
| publisher |
Public Library of Science |
| publishDate |
2023 |
| _version_ |
1806427478987636736 |
| score |
13.222552 |