Приказ основних података о документу

dc.creatorAntanasijević, Davor
dc.creatorRistić, Mirjana
dc.creatorPerić-Grujić, Aleksandra
dc.creatorPocajt, Viktor
dc.date.accessioned2021-03-10T12:07:45Z
dc.date.available2021-03-10T12:07:45Z
dc.date.issued2013
dc.identifier.issn0886-9383
dc.identifier.urihttp://TechnoRep.tmf.bg.ac.rs/handle/123456789/2401
dc.description.abstractA neural network model for predicting country-level concentrations of the fraction of particulates in the air with sizes less than 10 mu m (PM10) has been developed using widely available sustainability and economical/industrial parameters as inputs. The model was trained and validated with the data for 23 European Union (EU) countries plus the EU27 as a group for the period from 2000 to 2008. The inputs for the model were selected using correlation analyses. Country-level PM10 concentration data that were used as a model output were obtained from the World Bank. The artificial neural network (ANN) model, created with inputs chosen by correlation analyses, has shown very good performance in the forecast of country-level PM10 concentrations. The mean absolute error for the ANN model prediction, in the case of most of the EU countries, was less than 13%, indicating stable and accurate predictions. The predictions obtained from the principal component regression model, which was trained and tested using the same datasets and input variables, had mean absolute errors from 20% to 150% for most of the countries. The wide availability of input parameters used in this model can overcome the problem of lack and scarcity of data in many countries, which can in turn prevent the determination of human exposure to PM10 at the national level.en
dc.publisherWiley, Hoboken
dc.relationinfo:eu-repo/grantAgreement/MESTD/Basic Research (BR or ON)/172007/RS//
dc.rightsrestrictedAccess
dc.sourceJournal of Chemometrics
dc.subjectartificial neural networksen
dc.subjectPM10 country-level forecastingen
dc.subjectprincipal component regressionen
dc.titleForecasting human exposure to PM10 at the national level using an artificial neural network approachen
dc.typearticle
dc.rights.licenseARR
dc.citation.epage177
dc.citation.issue6
dc.citation.other27(6): 170-177
dc.citation.rankM21
dc.citation.spage170
dc.citation.volume27
dc.identifier.doi10.1002/cem.2505
dc.identifier.scopus2-s2.0-84878935220
dc.identifier.wos000320032600005
dc.type.versionpublishedVersion


Документи

Thumbnail

Овај документ се појављује у следећим колекцијама

Приказ основних података о документу