Publication:
Review of nitrogen compounds prediction in water bodies using artificial neural networks and other models

Simple item page
dc.citedby17
dc.contributor.authorKumar P.en_US
dc.contributor.authorLai S.H.en_US
dc.contributor.authorWong J.K.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.authorid57194870148en_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.accessioned2023-05-29T08:09:45Z
dc.date.available2023-05-29T08:09:45Z
dc.date.issued2020
dc.descriptionagricultural land; artificial neural network; complexity; concentration (composition); fertilizer application; nitrogen; optimization; prediction; stream; water quality; water treatmenten_US
dc.description.abstractThe 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.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo4359
dc.identifier.doi10.3390/su12114359
dc.identifier.issue11
dc.identifier.scopus2-s2.0-85085952776
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85085952776&doi=10.3390%2fsu12114359&partnerID=40&md5=70247d92f3f0e3958a5efbf5411ca71f
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/25466
dc.identifier.volume12
dc.publisherMDPIen_US
dc.relation.ispartofAll Open Access, Gold, Green
dc.sourceScopus
dc.sourcetitleSustainability (Switzerland)
dc.titleReview of nitrogen compounds prediction in water bodies using artificial neural networks and other modelsen_US
dc.typeReviewen_US
dspace.entity.typePublication
Files
Collections
SCOPUS