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Enhancement of nitrogen prediction accuracy through a new hybrid model using ant colony optimization and an Elman neural network

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dc.citedby3
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.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.authorid57214837520en_US
dc.contributor.authorid7005414714en_US
dc.contributor.authorid6505592467en_US
dc.contributor.authorid16068189400en_US
dc.date.accessioned2023-05-29T09:10:55Z
dc.date.available2023-05-29T09:10:55Z
dc.date.issued2021
dc.description.abstractAdvanced human activities, including�modern agricultural practices, are responsible for alteration of natural concentration of nitrogen compounds in rivers. Future prediction of nitrogen compound concentrations (especially nitrate-nitrogen and ammonia-nitrogen) are important for countries where household water is obtained from rivers after treatment. Increased concentrations of nitrogen compounds result in the suspension of household water supplies. Artificial Neural Networks (ANNs) have already been deployed for the prediction of nitrogen compounds in various countries. But standalone ANN have several limitations. However, the limitations of ANNs can be resolved using hybrid models. This study proposes a new ACO-ENN hybrid model developed by integrating Ant Colony Optimization (ACO) with an Elman Neural Network (ENN). The developed ACO-ENN hybrid model was used to improve the prediction results of nitrate-nitrogen and ammonia-nitrogen prediction models. The results of new hybrid models were compared with multilayer ANN models and standalone ENN models. There was a significant improvement in the mean square errors�(MSE)�(0.196?0.049?0.012, i.e. ANN?ENN?Hybrid), mean absolute errors�(MAE)�(0.271?0.094?0.069) and Nash�Sutcliffe efficiencies�(NSE)�(0.7255?0.9321?0.984). The hybrid model had outstanding performance compared with the ANN and ENN models. Hence, the prediction�accuracy�of nitrate-nitrogen and ammonia-nitrogen has been improved using new ACO-ENN hybrid model. � 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1080/19942060.2021.1990134
dc.identifier.epage1867
dc.identifier.issue1
dc.identifier.scopus2-s2.0-85120165210
dc.identifier.spage1843
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85120165210&doi=10.1080%2f19942060.2021.1990134&partnerID=40&md5=f1001468d1d2bd483794f9db2a6ef17c
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/26472
dc.identifier.volume15
dc.publisherTaylor and Francis Ltd.en_US
dc.relation.ispartofAll Open Access, Gold, Green
dc.sourceScopus
dc.sourcetitleEngineering Applications of Computational Fluid Mechanics
dc.titleEnhancement of nitrogen prediction accuracy through a new hybrid model using ant colony optimization and an Elman neural networken_US
dc.typeArticleen_US
dspace.entity.typePublication
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