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Performance Evaluation of Hydroponic Wastewater Treatment Plant Integrated with Ensemble Learning Techniques: A Feature Selection Approach

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dc.citedby5
dc.contributor.authorMustafa H.M.en_US
dc.contributor.authorHayder G.en_US
dc.contributor.authorAbba S.I.en_US
dc.contributor.authorAlgarni A.D.en_US
dc.contributor.authorMnzool M.en_US
dc.contributor.authorNour A.H.en_US
dc.contributor.authorid57217195204en_US
dc.contributor.authorid56239664100en_US
dc.contributor.authorid57208942739en_US
dc.contributor.authorid57204971671en_US
dc.contributor.authorid57852200500en_US
dc.contributor.authorid14719696000en_US
dc.date.accessioned2024-10-14T03:18:48Z
dc.date.available2024-10-14T03:18:48Z
dc.date.issued2023
dc.description.abstractWastewater treatment and reuse are being regarded as the most effective strategy for combating water scarcity threats. This study examined and reported the applications of the Internet of Things (IoT) and artificial intelligence in the phytoremediation of wastewater using Salvinia molesta plants. Water quality (WQ) indicators (total dissolved solids (TDS), temperature, oxidation-reduction potential (ORP), and turbidity) of the S. molesta treatment system at a retention time of 24 h were measured using an Arduino IoT device. Finally, four machine learning tools (ML) were employed in modeling and evaluating the predicted concentration of the total dissolved solids after treatment (TDSt) of the water samples. Additionally, three nonlinear error ensemble methods were used to enhance the prediction accuracy of the TDSt models. The outcome obtained from the modeling and prediction of the TDSt depicted that the best results were observed at SVM-M1 with 0.9999, 0.0139, 1.0000, and 0.1177 for R2, MSE, R, and RMSE, respectively, at the training stage. While at the validation stage, the R2, MSE, R, and RMSE were recorded as 0.9986, 0.0356, 0.993, and 0.1887, respectively. Furthermore, the error ensemble techniques employed significantly outperformed the single models in terms of mean square error (MSE) and root mean square error (RMSE) for both training and validation, with 0.0014 and 0.0379, respectively. � 2023 by the authors.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo478
dc.identifier.doi10.3390/pr11020478
dc.identifier.issue2
dc.identifier.scopus2-s2.0-85149251802
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85149251802&doi=10.3390%2fpr11020478&partnerID=40&md5=33311389956c428039d39c23b4d3a797
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/34281
dc.identifier.volume11
dc.publisherMultidisciplinary Digital Publishing Institute (MDPI)en_US
dc.relation.ispartofAll Open Access
dc.relation.ispartofGold Open Access
dc.sourceScopus
dc.sourcetitleProcesses
dc.subjectcomputational analysis
dc.subjectenergy
dc.subjecterror ensemble methods
dc.subjecttotal dissolved solids
dc.subjectwater quality forecasting
dc.subjectBiochemical oxygen demand
dc.subjectBiological water treatment
dc.subjectBioremediation
dc.subjectDissolved oxygen
dc.subjectErrors
dc.subjectForecasting
dc.subjectInternet of things
dc.subjectLearning systems
dc.subjectMean square error
dc.subjectPotable water
dc.subjectQuality control
dc.subjectRedox reactions
dc.subjectWastewater reclamation
dc.subjectWastewater treatment
dc.subjectWater conservation
dc.subjectAfter-treatment
dc.subjectComputational analysis
dc.subjectEnergy
dc.subjectEnsemble methods
dc.subjectError ensemble method
dc.subjectMeans square errors
dc.subjectRoot mean square errors
dc.subjectSquare-root
dc.subjectTotal dissolved solids
dc.subjectWater quality forecasting
dc.subjectWater quality
dc.titlePerformance Evaluation of Hydroponic Wastewater Treatment Plant Integrated with Ensemble Learning Techniques: A Feature Selection Approachen_US
dc.typeArticleen_US
dspace.entity.typePublication
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