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Enhancing riverine load prediction of anthropogenic pollutants: Harnessing the potential of feed-forward backpropagation (FFBP) artificial neural network (ANN) models

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dc.citedby4
dc.contributor.authorKhairudin K.en_US
dc.contributor.authorUl-Saufie A.Z.en_US
dc.contributor.authorSenin S.F.en_US
dc.contributor.authorZainudin Z.en_US
dc.contributor.authorRashid A.M.en_US
dc.contributor.authorAbu Bakar N.F.en_US
dc.contributor.authorAnas Abd Wahid M.Z.en_US
dc.contributor.authorAzha S.F.en_US
dc.contributor.authorAbd-Wahab F.en_US
dc.contributor.authorWang L.en_US
dc.contributor.authorSahar F.N.en_US
dc.contributor.authorOsman M.S.en_US
dc.contributor.authorid57838061200en_US
dc.contributor.authorid55358162200en_US
dc.contributor.authorid56644623000en_US
dc.contributor.authorid59052812100en_US
dc.contributor.authorid57205730172en_US
dc.contributor.authorid35110898100en_US
dc.contributor.authorid58979033300en_US
dc.contributor.authorid56166798400en_US
dc.contributor.authorid57208972231en_US
dc.contributor.authorid59448610500en_US
dc.contributor.authorid58979033400en_US
dc.contributor.authorid55319956200en_US
dc.date.accessioned2025-03-03T07:43:15Z
dc.date.available2025-03-03T07:43:15Z
dc.date.issued2024
dc.description.abstractAssessing riverine pollutant loads is a more realistic method for analysing point and non-point anthropogenic pollution sources throughout a watershed. This study compares numerous mathematical modelling strategies for estimating riverine loads based on the chosen water quality parameters: Biochemical Oxygen Demand (BOD), Chemical Oxygen Demand (COD), Suspended Solids (SS), and Ammoniacal Nitrogen (NH3?N). A riverine load model was developed by employing various input variables including river flow and pollutant concentration values collected at several monitoring sites. Among the mathematical modelling methods employed are artificial neural networks with feed-forward backpropagation algorithms and radial basis functions. The classical multiple linear regression (MLR) statistical model was used for the comparison. Four widely used statistical performance assessment metrics were adopted to evaluate the performance of the various developed models: the root mean square error (RMSE), mean absolute error (MAE), mean relative error (MRE), and coefficient of determination (R2). The considerable number of errors (with RMSE, MAE, and MRE) discovered in estimating riverine loads using the multiple linear regression (MLR) statistical model can be attributed to the nonlinear relationship between the independent variables (Q and Cx) and dependent variables (W). The feed-forward neural network model with a backpropagation algorithm and Bayesian regularisation training algorithm outperformed the radial basis neural network. This finding implies that, in addition to suspended sediment loads, riverine loads may be predicted using an artificial neural network using pollutant concentration (Cx) and river discharge (Q) as input variables. Other geographical and temporal fluctuation characteristics that may impact river water quality, on the other hand, may be incorporated as input variables to enhance riverine load prediction. Finally, riverine load analyses were successfully conducted to reduce the riverine load. ? 2024 The Author(s)en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo102072
dc.identifier.doi10.1016/j.rineng.2024.102072
dc.identifier.scopus2-s2.0-85189939391
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85189939391&doi=10.1016%2fj.rineng.2024.102072&partnerID=40&md5=1b41e5180e4e2c5be04e5a5ecaa86eed
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/36588
dc.identifier.volume22
dc.publisherElsevier B.V.en_US
dc.relation.ispartofAll Open Access; Gold Open Access
dc.sourceScopus
dc.sourcetitleResults in Engineering
dc.subjectAmmonia
dc.subjectBiochemical oxygen demand
dc.subjectDissolved oxygen
dc.subjectErrors
dc.subjectMean square error
dc.subjectRadial basis function networks
dc.subjectRiver pollution
dc.subjectRivers
dc.subjectSuspended sediments
dc.subjectWater quality
dc.subjectArtificial neural network
dc.subjectFeed-forward backpropagation algorithm
dc.subjectFeedforward backpropagation
dc.subjectInput variables
dc.subjectLoad predictions
dc.subjectMultiple linear regressions
dc.subjectPollutant concentration
dc.subjectRadial basis neural networks
dc.subjectRiverine load
dc.subjectStatistic modeling
dc.subjectMultiple linear regression
dc.titleEnhancing riverine load prediction of anthropogenic pollutants: Harnessing the potential of feed-forward backpropagation (FFBP) artificial neural network (ANN) modelsen_US
dc.typeArticleen_US
dspace.entity.typePublication
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