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Water Quality Predictive Analytics Using an Artificial Neural Network with a Graphical User Interface

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dc.citedby5
dc.contributor.authorRizal N.N.M.en_US
dc.contributor.authorHayder G.en_US
dc.contributor.authorYusof K.A.en_US
dc.contributor.authorid57654708600en_US
dc.contributor.authorid56239664100en_US
dc.contributor.authorid57208124488en_US
dc.date.accessioned2023-05-29T09:37:45Z
dc.date.available2023-05-29T09:37:45Z
dc.date.issued2022
dc.descriptionCase based reasoning; Forecasting; Graphical user interfaces; Machine learning; Neural networks; Predictive analytics; River pollution; Rivers; Subroutines; Well testing; Anthropogenic sources; Artificial neural network modeling; Clean waters; Daily lives; Fresh Water; Living things; Natural sources; Real time streaming; River water quality; Water quality parameters; Water quality; artificial neural network; pollutant source; river water; water quality; Langat River; Malaysia; West Malaysiaen_US
dc.description.abstractSince clean water is well known as one of the crucial sources that all living things need in their daily lives, the demand for clean freshwater nowadays has increased. However, water quality is slowly deteriorating due to anthropogenic and natural sources of pollution and contamination. Therefore, this study aims to develop artificial neural network (ANN) models to predict six different water quality parameters in the Langat River, Malaysia. Moreover, an application (app) equipped with a graphical user interface (GUI) was designed and developed to conduct real-time prediction of the water quality parameters by using real-time data as inputs together with the ANN models. As for the results, all of the ANN models achieved high coefficients of determination (R2), which were between 0.9906 and 0.9998, as well as between 0.8797 and 0.9972 for training and testing datasets, respectively. The developed app successfully predicted the outcome based on the run models. The implementation of a GUI-based app in this study enables a simpler and more trouble-free workflow in predicting water quality parameters. By eliminating sophisticated programming subroutines, the prediction process becomes accessible to more people, especially on-site operators and trainees. � 2022 by the authors. Licensee MDPI, Basel, Switzerland.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo1221
dc.identifier.doi10.3390/w14081221
dc.identifier.issue8
dc.identifier.scopus2-s2.0-85129019844
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85129019844&doi=10.3390%2fw14081221&partnerID=40&md5=f3a3f3187f76040783d2408dbbc18e51
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/26917
dc.identifier.volume14
dc.publisherMDPIen_US
dc.relation.ispartofAll Open Access, Gold
dc.sourceScopus
dc.sourcetitleWater (Switzerland)
dc.titleWater Quality Predictive Analytics Using an Artificial Neural Network with a Graphical User Interfaceen_US
dc.typeArticleen_US
dspace.entity.typePublication
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