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dc.contributor.authorRustam, Furqan
dc.contributor.authorIshaq, Abid
dc.contributor.authorKokab, Sayyida Tabinda
dc.contributor.authorTorre Díez, Isabel de la 
dc.contributor.authorVidal Mazón, Juan Luis
dc.contributor.authorRodríguez, Carmen Lili
dc.contributor.authorAshraf, Imran
dc.date.accessioned2023-09-19T10:55:05Z
dc.date.available2023-09-19T10:55:05Z
dc.date.issued2022
dc.identifier.citationWater, 2022, Vol. 14, Nº. 21, 3359es
dc.identifier.issn2073-4441es
dc.identifier.urihttps://uvadoc.uva.es/handle/10324/61641
dc.descriptionProducción Científicaes
dc.description.abstractWith rapid urbanization, high rates of industrialization, and inappropriate waste disposal, water quality has been substantially degraded during the past decade. So, water quality prediction, an essential element for a healthy society, has become a task of great significance to protecting the water environment. Existing approaches focus predominantly on either water quality or water consumption prediction, utilizing complex algorithms that reduce the accuracy of imbalanced datasets and increase computational complexity. This study proposes a simple architecture of neural networks which is more efficient and accurate and can work for predicting both water quality and water consumption. An artificial neural network (ANN) consisting of one hidden layer and a couple of dropout and activation layers is utilized in this regard. The approach is tested using two datasets for predicting water quality and water consumption. Results show a 0.96 accuracy for water quality prediction which is better than existing studies. A 0.99 R2 score is obtained for water consumption prediction which is superior to existing state-of-the-art approaches.es
dc.format.mimetypeapplication/pdfes
dc.language.isoenges
dc.publisherMDPIes
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectWater qualityes
dc.subjectWater quality monitoringes
dc.subjectAgua - Calidad - Controles
dc.subjectWater consumptiones
dc.subjectAgua - Consumoes
dc.subjectWater-supplyes
dc.subjectAgua - Abastecimientoes
dc.subjectNeural networks (Computer science)es
dc.subjectRedes neuronales (Informática)es
dc.subjectClassificationes
dc.subjectArtificial intelligence
dc.titleAn artificial neural network model for water quality and water consumption predictiones
dc.typeinfo:eu-repo/semantics/articlees
dc.rights.holder© 2022 The Authorses
dc.identifier.doi10.3390/w14213359es
dc.relation.publisherversionhttps://www.mdpi.com/2073-4441/14/21/3359es
dc.identifier.publicationfirstpage3359es
dc.identifier.publicationissue21es
dc.identifier.publicationtitleWateres
dc.identifier.publicationvolume14es
dc.peerreviewedSIes
dc.identifier.essn2073-4441es
dc.rightsAtribución 4.0 Internacional*
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersiones
dc.subject.unesco1203.04 Inteligencia Artificiales
dc.subject.unesco2508.11 Calidad de las Aguases


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