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Water quality prediction model based support vector machine model for ungauged river catchment under dual scenarios

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dc.citedby66
dc.contributor.authorYahya A.S.A.en_US
dc.contributor.authorAhmed A.N.en_US
dc.contributor.authorOthman F.B.en_US
dc.contributor.authorIbrahim R.K.en_US
dc.contributor.authorAfan H.A.en_US
dc.contributor.authorEl-Shafie A.en_US
dc.contributor.authorFai C.M.en_US
dc.contributor.authorHossain M.S.en_US
dc.contributor.authorEhteram M.en_US
dc.contributor.authorElshafie A.en_US
dc.contributor.authorid57206338836en_US
dc.contributor.authorid57214837520en_US
dc.contributor.authorid36630785100en_US
dc.contributor.authorid57188832586en_US
dc.contributor.authorid56436626600en_US
dc.contributor.authorid57207789882en_US
dc.contributor.authorid57214146115en_US
dc.contributor.authorid55579596900en_US
dc.contributor.authorid57113510800en_US
dc.contributor.authorid16068189400en_US
dc.date.accessioned2023-05-29T07:25:24Z
dc.date.available2023-05-29T07:25:24Z
dc.date.issued2019
dc.descriptionAmmonia; Biochemical oxygen demand; Catchments; Dissolved oxygen; Forecasting; Reservoirs (water); River pollution; Rivers; Runoff; Support vector machines; Water pollution control; Water quality; 10-fold cross-validation; Biochemical oxygen demands (BOD); Correlation coefficient; Environmental standards; Support vector machine models; Water quality parameters; Water quality predictions; Water resources management; Quality control; accuracy assessment; catchment; error analysis; model validation; numerical model; prediction; support vector machine; water quality; Langat Basin; Malaysia; West Malaysiaen_US
dc.description.abstractWater quality analysis is a crucial step in water resources management and needs to be addressed urgently to control any pollution that may adversely affect the ecosystem and to ensure the environmental standards are being met. Thus, this work is an attempt to develop an efficient model using support vector machine (SVM) to predict the water quality of Langat River Basin through the analysis of the data of six parameters of dual reservoirs that are located in the catchment. The proposed model could be considered as an effective tool for identifying the water quality status for the river catchment area. In addition, the major advantage of the proposed model is that it could be useful for ungauged catchments or those lacking enough numbers of monitoring stations for water quality parameters. These parameters, namely pH, Suspended Solids (SS), Dissolved Oxygen (DO), Ammonia Nitrogen (AN), Chemical Oxygen Demand (COD), and Biochemical Oxygen Demand (BOD) were provided by the Malaysian Department of Environment (DOE). The differences between dual scenarios 1 and 2 depend on the information from prior stations to forecast DO levels for succeeding sites (Scenario 2). This scheme has the capacity to simulate water-quality accurately, with small prediction errors. The resulting correlation coefficient has maximum values of 0.998 and 0.979 after the application of Scenario 1. The approach with Type 1 SVM regression along with 10-fold cross-validation methods worked to generate precise results. The MSE value was found to be between 0.004 and 0.681, with Scenario 1 showing a better outcome. � 2019 by the authors.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo1231
dc.identifier.doi10.3390/w11061231
dc.identifier.issue6
dc.identifier.scopus2-s2.0-85068884314
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85068884314&doi=10.3390%2fw11061231&partnerID=40&md5=139cd3784f887c246833d61b86709320
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/24641
dc.identifier.volume11
dc.publisherMDPI AGen_US
dc.relation.ispartofAll Open Access, Gold, Green
dc.sourceScopus
dc.sourcetitleWater (Switzerland)
dc.titleWater quality prediction model based support vector machine model for ungauged river catchment under dual scenariosen_US
dc.typeArticleen_US
dspace.entity.typePublication
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