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Towards a time and cost effective approach to water quality index class prediction

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dc.citedby64
dc.contributor.authorHo J.Y.en_US
dc.contributor.authorAfan H.A.en_US
dc.contributor.authorEl-Shafie A.H.en_US
dc.contributor.authorKoting S.B.en_US
dc.contributor.authorMohd N.S.en_US
dc.contributor.authorJaafar W.Z.B.en_US
dc.contributor.authorLai Sai H.en_US
dc.contributor.authorMalek M.A.en_US
dc.contributor.authorAhmed A.N.en_US
dc.contributor.authorMohtar W.H.M.W.en_US
dc.contributor.authorElshorbagy A.en_US
dc.contributor.authorEl-Shafie A.en_US
dc.contributor.authorid57208900599en_US
dc.contributor.authorid56436626600en_US
dc.contributor.authorid57207789882en_US
dc.contributor.authorid55839645200en_US
dc.contributor.authorid57192892703en_US
dc.contributor.authorid55006925400en_US
dc.contributor.authorid57208898459en_US
dc.contributor.authorid55636320055en_US
dc.contributor.authorid57214837520en_US
dc.contributor.authorid25637975300en_US
dc.contributor.authorid6602558230en_US
dc.contributor.authorid16068189400en_US
dc.date.accessioned2023-05-29T07:24:26Z
dc.date.available2023-05-29T07:24:26Z
dc.date.issued2019
dc.descriptionAmmonia; Biochemical oxygen demand; Cost effectiveness; Data mining; Decision trees; Dissolved oxygen; Forecasting; Learning systems; Quality assurance; Rivers; Water conservation; Water management; Water quality; Biochemical oxygen demands (BOD); Cost-effective approach; Decision tree modeling; Prediction model; River water quality; Water quality indexes; Water quality parameters; Water quality predictions; River pollution; accuracy assessment; cost analysis; decision analysis; experimental study; hydrological modeling; index method; machine learning; numerical model; parameter estimation; prediction; river management; river water; water quality; Klang River; Malaysia; West Malaysiaen_US
dc.description.abstractThe development of water quality prediction models is an important step towards better water quality management of rivers. The traditional method for computing WQI is always associated with errors due to the protracted analysis of the water quality parameters in addition to the great effort and time involved in gathering and analyzing water samples. In addition, the cost of identifying the magnitude of some of the parameters through experimental testing is very high. The water quality of rivers in Malaysia is ranked into five classes based on water quality index (WQI). WQI is function of six water quality parameters: ammoniac nitrogen (NH3-N), biochemical oxygen demand (BOD), chemical oxygen demand (COD), dissolved oxygen (DO), pH, and suspended solids (SS). In this research, the decision tree machine learning technique is used to predict the WQI for the Klang River and its classification within a specific water quality class. Klang River is one of the most polluted rivers in Malaysia. Modeling experiments are designed to test the prediction and classification accuracy of the model based on various scenarios composed of different water quality parameters. Results show that the proposed prediction model has a promising potential to predict the class of the WQI. Moreover, the proposed model offers a more efficient process and cost-effective approach for the computation and prediction of WQI. � 2019 Elsevier B.V.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1016/j.jhydrol.2019.05.016
dc.identifier.epage165
dc.identifier.scopus2-s2.0-85066089349
dc.identifier.spage148
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85066089349&doi=10.1016%2fj.jhydrol.2019.05.016&partnerID=40&md5=20948fb8697e3dd3c44e971d95f0ef6c
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/24547
dc.identifier.volume575
dc.publisherElsevier B.V.en_US
dc.sourceScopus
dc.sourcetitleJournal of Hydrology
dc.titleTowards a time and cost effective approach to water quality index class predictionen_US
dc.typeArticleen_US
dspace.entity.typePublication
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