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Efficient river water quality index prediction considering minimal number of inputs variables

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dc.citedby35
dc.contributor.authorOthman F.en_US
dc.contributor.authorAlaaeldin M.E.en_US
dc.contributor.authorSeyam M.en_US
dc.contributor.authorAhmed A.N.en_US
dc.contributor.authorTeo F.Y.en_US
dc.contributor.authorMing Fai C.en_US
dc.contributor.authorAfan H.A.en_US
dc.contributor.authorSherif M.en_US
dc.contributor.authorSefelnasr A.en_US
dc.contributor.authorEl-Shafie A.en_US
dc.contributor.authorid36630785100en_US
dc.contributor.authorid57217306176en_US
dc.contributor.authorid56182818000en_US
dc.contributor.authorid57214837520en_US
dc.contributor.authorid35249518400en_US
dc.contributor.authorid57214146115en_US
dc.contributor.authorid56436626600en_US
dc.contributor.authorid7005414714en_US
dc.contributor.authorid6505592467en_US
dc.contributor.authorid16068189400en_US
dc.date.accessioned2023-05-29T08:13:06Z
dc.date.available2023-05-29T08:13:06Z
dc.date.issued2020
dc.description.abstractWater Quality Index (WQI) is the most common determinant of the quality of the stream-flow. According to the Department of Environment (DOE, Malaysia), WQI is chiefly affected by six factors, which are, chemical oxygen demand�(COD), biochemical oxygen demand�(BOD), dissolved oxygen�(DO), suspended solids�(SS), -potential for hydrogen (pH), and ammoniacal nitrogen�(AN). In fact, understanding the inter-relationships between these variables and WQI can improve predicting the WQI for better water resources management. The aim of this study is to create an input approach using ANNs (Artificial Neural Networks) to compute the WQI from input parameters instead of using the indices of the parameters when one of the parameters is absent. The data are collected from the nine water quality monitoring stations at the Klang River basin, Malaysia. In addition, comprehensive sensitivity analysis has been carried out to identify the most influential input parameters. The model is based on the frequency distribution of the significant factors showed exceptional ability to replicate the WQI and attained very high correlation (98.78%). Furthermore, the sensitivity analysis showed that the most influential parameter that affects WQI is DO, while pH is the least one. Additionally, the performance of models shows that the missing DO values caused deterioration in the accuracy. � 2020, � 2020 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1080/19942060.2020.1760942
dc.identifier.epage763
dc.identifier.issue1
dc.identifier.scopus2-s2.0-85087044977
dc.identifier.spage751
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85087044977&doi=10.1080%2f19942060.2020.1760942&partnerID=40&md5=e0847b8394a01f3758bc3021bd3d685f
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/25708
dc.identifier.volume14
dc.publisherTaylor and Francis Ltd.en_US
dc.relation.ispartofAll Open Access, Gold
dc.sourceScopus
dc.sourcetitleEngineering Applications of Computational Fluid Mechanics
dc.titleEfficient river water quality index prediction considering minimal number of inputs variablesen_US
dc.typeArticleen_US
dspace.entity.typePublication
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