Publication:
Monitoring and modelling of water quality parameters using artificial intelligence

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dc.citedby1
dc.contributor.authorOmar D.P.M.A.en_US
dc.contributor.authorHayder G.en_US
dc.contributor.authorHung Y.-T.en_US
dc.contributor.authorid58313272000en_US
dc.contributor.authorid56239664100en_US
dc.contributor.authorid7201351881en_US
dc.date.accessioned2024-10-14T03:20:46Z
dc.date.available2024-10-14T03:20:46Z
dc.date.issued2023
dc.description.abstractRapid population growth leads to an increase in demand for water and spikes levels of water pollution. In this study, a low cost and innovative internet of things (IoT) device was used in the monitoring of water quality parameters. The monitoring system implemented used consists of maker-UNO as the core controller, SIM7600-GSM module as the Wi-Fi module and the water quality parameters sensors (total dissolved solids (TDS), oxidation reduction potential (ORP), temperature and turbidity). This study applied five different artificial intelligence (AI) techniques models to predict the water quality parameters. The data were collected from phytoremediation treatment system and modelled by using artificial neural network (ANN), regression trees, support vector machine (SVM), ensemble trees and the Gaussian process regression (GPR). A satisfying prediction models were achieved indicating that early prevention of contamination in the treatment system can be achieved through the application of monitoring and artificial intelligence modelling tools. Copyright � 2023 Inderscience Enterprises Ltd.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1504/IJEWM.2023.131153
dc.identifier.epage533
dc.identifier.issue4
dc.identifier.scopus2-s2.0-85161866294
dc.identifier.spage525
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85161866294&doi=10.1504%2fIJEWM.2023.131153&partnerID=40&md5=66d0a8783982c9400a19f2c01567d34f
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/34573
dc.identifier.volume31
dc.pagecount8
dc.publisherInderscience Publishersen_US
dc.sourceScopus
dc.sourcetitleInternational Journal of Environment and Waste Management
dc.subjectartificial intelligence
dc.subjectmonitoring
dc.subjectprediction model
dc.subjectwater quality
dc.subjectBioremediation
dc.subjectForecasting
dc.subjectGlobal system for mobile communications
dc.subjectInternet of things
dc.subjectNeural networks
dc.subjectPopulation statistics
dc.subjectRedox reactions
dc.subjectSupport vector machines
dc.subjectWater pollution
dc.subjectLow-costs
dc.subjectModel of water quality
dc.subjectMonitoring system
dc.subjectOxidation-reduction potentials
dc.subjectPrediction modelling
dc.subjectRapid population growth
dc.subjectSolid oxidation
dc.subjectTotal dissolved solids
dc.subjectTreatment systems
dc.subjectWater quality parameters
dc.subjectWater quality
dc.titleMonitoring and modelling of water quality parameters using artificial intelligenceen_US
dc.typeArticleen_US
dspace.entity.typePublication
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