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Optimised neural network model for river-nitrogen prediction utilizing a new training approach

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dc.citedby12
dc.contributor.authorKumar P.en_US
dc.contributor.authorLai S.H.en_US
dc.contributor.authorMohd N.S.en_US
dc.contributor.authorKamal M.R.en_US
dc.contributor.authorAfan H.A.en_US
dc.contributor.authorAhmed A.N.en_US
dc.contributor.authorSherif M.en_US
dc.contributor.authorSefelnasr A.en_US
dc.contributor.authorEl-Shafie A.en_US
dc.contributor.authorid57206939156en_US
dc.contributor.authorid36102664300en_US
dc.contributor.authorid57192892703en_US
dc.contributor.authorid6507669917en_US
dc.contributor.authorid56436626600en_US
dc.contributor.authorid57214837520en_US
dc.contributor.authorid7005414714en_US
dc.contributor.authorid6505592467en_US
dc.contributor.authorid16068189400en_US
dc.date.accessioned2023-05-29T08:07:39Z
dc.date.available2023-05-29T08:07:39Z
dc.date.issued2020
dc.descriptionammonia; 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 Qualityen_US
dc.description.abstractIn 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.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNoe0239509
dc.identifier.doi10.1371/journal.pone.0239509
dc.identifier.issue9-Sep
dc.identifier.scopus2-s2.0-85092050972
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85092050972&doi=10.1371%2fjournal.pone.0239509&partnerID=40&md5=bcd0108855dfe7e7fc594e321690f040
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/25260
dc.identifier.volume15
dc.publisherPublic Library of Scienceen_US
dc.relation.ispartofAll Open Access, Gold, Green
dc.sourceScopus
dc.sourcetitlePLoS ONE
dc.titleOptimised neural network model for river-nitrogen prediction utilizing a new training approachen_US
dc.typeArticleen_US
dspace.entity.typePublication
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