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dc.creatorŠiljić-Tomić, Aleksandra
dc.creatorAntanasijević, Davor
dc.creatorRistić, Mirjana
dc.creatorPerić-Grujić, Aleksandra
dc.creatorPocajt, Viktor
dc.date.accessioned2021-03-10T13:49:04Z
dc.date.available2021-03-10T13:49:04Z
dc.date.issued2018
dc.identifier.issn0048-9697
dc.identifier.urihttp://TechnoRep.tmf.bg.ac.rs/handle/123456789/3974
dc.description.abstractAccurate prediction of water quality parameters (WQPs) is an important task in the management of water resources. Artificial neural networks (ANNs) are frequently applied for dissolved oxygen (DO) prediction, but often only their interpolation performance is checked. The aims of this research, beside interpolation, were the determination of extrapolation performance of ANN model, which was developed for the prediction of DO content in the Danube River, and the assessment of relationship between the significance of inputs and prediction error in the presence of values which were of out of the range of training. The applied ANN is a polynomial neural network (PNN) which performs embedded selection of most important inputs during learning, and provides a model in the form of linear and non-linear polynomial functions, which can then be used for a detailed analysis of the significance of inputs. Available dataset that contained 1912 monitoring records for 17 water quality parameters was split into a "regular" subset that contains normally distributed and low variability data, and an "extreme" subset that contains monitoring records with outlier values. The results revealed that the non-linear PNN model has good interpolation performance (R-2 = 0.82), but it was not robust in extrapolation (R-2 = 0.63). The analysis of extrapolation results has shown that the prediction errors are correlated with the significance of inputs. Namely, the out-of-training range values of the inputs with low importance do not affect significantly the PNN model performance, but their influence can be biased by the presence of multi-outlier monitoring records. Subsequently, linear PNN models were successfully applied to study the effect of water quality parameters on DO content. It was observed that DO level is mostly affected by temperature, pH, biological oxygen demand (BOD) and phosphorus concentration, while in extreme conditions the importance of alkalinity and bicarbonates rises over pH and BOD.en
dc.publisherElsevier Science Bv, Amsterdam
dc.relationinfo:eu-repo/grantAgreement/MESTD/Basic Research (BR or ON)/172007/RS//
dc.rightsrestrictedAccess
dc.sourceScience of the Total Environment
dc.subjectDissolved oxygenen
dc.subjectExtrapolationen
dc.subjectPNNen
dc.subjectGMDHen
dc.subjectDanube Riveren
dc.titleA linear and non-linear polynomial neural network modeling of dissolved oxygen content in surface water: Inter- and extrapolation performance with inputs' significance analysisen
dc.typearticle
dc.rights.licenseARR
dc.citation.epage1046
dc.citation.other610: 1038-1046
dc.citation.rankM21
dc.citation.spage1038
dc.citation.volume610
dc.identifier.doi10.1016/j.scitotenv.2017.08.192
dc.identifier.pmid28847097
dc.identifier.rcubconv_5409
dc.identifier.scopus2-s2.0-85027850174
dc.identifier.wos000411897700106
dc.type.versionpublishedVersion


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