Приказ основних података о документу

dc.creatorSiljić, Aleksandra
dc.creatorAntanasijević, Davor
dc.creatorPerić-Grujić, Aleksandra
dc.creatorRistić, Mirjana
dc.creatorPocajt, Viktor
dc.date.accessioned2021-03-10T12:48:00Z
dc.date.available2021-03-10T12:48:00Z
dc.date.issued2015
dc.identifier.issn0944-1344
dc.identifier.urihttp://TechnoRep.tmf.bg.ac.rs/handle/123456789/3033
dc.description.abstractBiological oxygen demand (BOD) is the most significant water quality parameter and indicates water pollution with respect to the present biodegradable organic matter content. European countries are therefore obliged to report annual BOD values to Eurostat; however, BOD data at the national level is only available for 28 of 35 listed European countries for the period prior to 2008, among which 46 % of data is missing. This paper describes the development of an artificial neural network model for the forecasting of annual BOD values at the national level, using widely available sustainability and economical/industrial parameters as inputs. The initial general regression neural network (GRNN) model was trained, validated and tested utilizing 20 inputs. The number of inputs was reduced to 15 using the Monte Carlo simulation technique as the input selection method. The best results were achieved with the GRNN model utilizing 25 % less inputs than the initial model and a comparison with a multiple linear regression model trained and tested using the same input variables using multiple statistical performance indicators confirmed the advantage of the GRNN model. Sensitivity analysis has shown that inputs with the greatest effect on the GRNN model were (in descending order) precipitation, rural population with access to improved water sources, treatment capacity of wastewater treatment plants (urban) and treatment of municipal waste, with the last two having an equal effect. Finally, it was concluded that the developed GRNN model can be useful as a tool to support the decision-making process on sustainable development at a regional, national and international level.en
dc.publisherSpringer Heidelberg, Heidelberg
dc.relationinfo:eu-repo/grantAgreement/MESTD/Basic Research (BR or ON)/172007/RS//
dc.rightsrestrictedAccess
dc.sourceEnvironmental Science and Pollution Research
dc.subjectGRNNen
dc.subjectBODen
dc.subjectRiver wateren
dc.subjectMCSen
dc.subjectMLRen
dc.subjectSustainabilityen
dc.titleArtificial neural network modelling of biological oxygen demand in rivers at the national level with input selection based on Monte Carlo simulationsen
dc.typearticle
dc.rights.licenseARR
dc.citation.epage4241
dc.citation.issue6
dc.citation.other22(6): 4230-4241
dc.citation.rankM21
dc.citation.spage4230
dc.citation.volume22
dc.identifier.doi10.1007/s11356-014-3669-y
dc.identifier.pmid25280507
dc.identifier.scopus2-s2.0-84925516159
dc.identifier.wos000350572500019
dc.type.versionpublishedVersion


Документи

Thumbnail

Овај документ се појављује у следећим колекцијама

Приказ основних података о документу