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The prediction of blue water footprint at Semambu water treatment plant by means of Artificial Neural Networks (ANN) and Support Vector Machine (SVM) models

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dc.citedby7
dc.contributor.authorMoni S.en_US
dc.contributor.authorAziz E.en_US
dc.contributor.authorAbdul Majeed A.P.P.en_US
dc.contributor.authorMalek M.en_US
dc.contributor.authorid57199181376en_US
dc.contributor.authorid57193070637en_US
dc.contributor.authorid57189582455en_US
dc.contributor.authorid55636320055en_US
dc.date.accessioned2023-05-29T09:05:52Z
dc.date.available2023-05-29T09:05:52Z
dc.date.issued2021
dc.descriptionForecasting; Mean square error; Predictive analytics; Rivers; Support vector machines; Sustainable development; Water treatment; Water treatment plants; Coefficient of determination; Hyperparameters; Influencing parameters; Mean squared error; Prediction model; Prediction performance; Water footprint; Waterresource management; Neural networks; artificial neural network; footprint; support vector machine; Sustainable Development Goal; water management; water resource; water supply; water treatment; Kuantan River; Malaysia; Pahang; West Malaysiaen_US
dc.description.abstractThe prediction of the blue water footprint in water services such as in water treatment plants (WTPs) is non-trivial to water resource management. Currently, the sustainability of water resources is of great concern globally, particularly in addressing the 6th goal of the United Nation's Sustainable Development Goals (UN SDGs). This study focuses on the blue water footprint (WFblue) assessment and prediction of WTP located at the Kuantan River Basin, Malaysia. The intake water of WTP is directly obtained from the mainstream river within the basin known as the Kuantan River. The predictability of the WFblue was evaluated by means of Artificial Neural Networks (ANN) and Support Vector Machine (SVM). Different hyperparameters of both the ANN and SVM models were investigated to ascertain the best prediction models attainable by evaluating both the mean squared error (MSE) as well as the coefficient of determination, R. It was demonstrated from the study that the optimised ANN model is able to yield a better prediction performance in comparison to the optimised SVM model. Therefore, it could be concluded that the application of ANN to predict the future trend is pertinent and should be incorporated in water footprint studies as it is vital for water resources regulators to anticipate the condition of WFblue in the future and to line up the appropriate actions especially in controlling the influencing parameters namely, water intake, rainfall and evaporation. � 2021en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo103052
dc.identifier.doi10.1016/j.pce.2021.103052
dc.identifier.scopus2-s2.0-85110436487
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85110436487&doi=10.1016%2fj.pce.2021.103052&partnerID=40&md5=75b9144642e42b26ff76eb8572ca34f8
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/25980
dc.identifier.volume123
dc.publisherElsevier Ltden_US
dc.sourceScopus
dc.sourcetitlePhysics and Chemistry of the Earth
dc.titleThe prediction of blue water footprint at Semambu water treatment plant by means of Artificial Neural Networks (ANN) and Support Vector Machine (SVM) modelsen_US
dc.typeArticleen_US
dspace.entity.typePublication
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