Repository logo
 
Publication

Artificial neural network modelling of the amount of separately-collected household packaging waste

2019-02Journal article Restricted access
Simple item page
dc.contributor.authorOliveira, Verónica
dc.contributor.authorSousa, Vitor
dc.contributor.authorFerreira, Célia
dc.date.accessioned2020-03-03T16:11:01Z
dc.date.available2020-03-03T16:11:01Z
dc.date.issued2019-02
dc.date.updated2020-02-29T11:38:01Z
dc.description.abstractThis work develops an artificial neural network (ANN) model using genetic algorithms to estimate the annual amount (kg/inhabitant/year) of separately-collected household packaging waste. The ANN model comprises one input layer, one hidden layer with seven neurons and one output layer. Ten variables affecting the amount of separately-collected packaging waste were identified and used in the ANN model. These variables are related to the level of education of the population, the size and level of urbanisation of the municipality, social aspects related to poverty and economic power and factors intrinsic to the waste collection service. A comparison between ANN and regression models for the estimation of packaging waste is also carried out. The performance of the proposed ANN model for a data set of 42 municipalities located in the centre of Portugal, measured by the R2 , is 0.98. This value is 34% higher than the best regression model applied to the same data set (R2 ¼ 0.73), indicating that ANN has a significantly higher explanatory power than traditional regression techniques. Another advantage is that ANN is not as sensitive to outliers as regression. However, ANN is more complex, has a higher number of variables, and the model development and interpretation of the results are more difficult. Nevertheless, the higher performance of ANN makes it a valuable tool in the definition of strategies to increase recycling and achieve circular economy goals.pt_PT
dc.description.sponsorshipCelia Dias-Ferreira and Veronica Oliveira have been funded through FCT “Fundação para a Ciência e para a Tecnologia ^ ” by POCH e Programa Operacional Capital Humano within ESF e European Social Fund and by national funds from MCTES (SFRH/BPD/100717/2014; SFRH/BD/115312/2016). Vitor Sousa acknowledge the support from FCT and CERIS. The authors also gratefully acknowledge project Life PAYT e “Tool to Reduce Waste in South Europe” (LIFE15ENV/PT/000609) for technical support.pt_PT
dc.description.sponsorshipLIFE15 ENV/PT/000609
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.1016/j.jclepro.2018.11.063pt_PT
dc.identifier.eid2-s2.0-85056584456
dc.identifier.issn0959-6526
dc.identifier.slugcv-prod-538309
dc.identifier.urihttp://hdl.handle.net/10400.2/9423
dc.language.isoporpt_PT
dc.peerreviewedyespt_PT
dc.relationRAW - Recovering nutrients from wastes: eco-innovative solutions to transform waste into resources
dc.relationPhosphorus recovery from organic wastes aiming at its valorisation as a fertilizer
dc.subjectMunicipal solid wastept_PT
dc.subjectANNpt_PT
dc.subjectGenetic algorithmpt_PT
dc.subjectRegression modelpt_PT
dc.subjectUrban wastept_PT
dc.subjectRecyclingpt_PT
dc.titleArtificial neural network modelling of the amount of separately-collected household packaging wastept_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleRAW - Recovering nutrients from wastes: eco-innovative solutions to transform waste into resources
oaire.awardTitlePhosphorus recovery from organic wastes aiming at its valorisation as a fertilizer
oaire.awardURIinfo:eu-repo/grantAgreement/FCT//SFRH%2FBPD%2F100717%2F2014/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/POR_CENTRO/SFRH%2FBD%2F115312%2F2016/PT
oaire.citation.endPage409pt_PT
oaire.citation.startPage401pt_PT
oaire.citation.titleJournal of Cleaner Productionpt_PT
oaire.citation.volume210pt_PT
oaire.fundingStreamPOR_CENTRO
person.familyNameCosta Oliveira
person.familyNameFerreira
person.givenNameVerónica
person.givenNameCélia
person.identifier.ciencia-id6318-20F3-86AF
person.identifier.ciencia-id931E-FBDE-2098
person.identifier.orcid0000-0001-6012-920X
person.identifier.orcid0000-0002-7456-2538
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.cv.cienciaid931E-FBDE-2098 | Célia Maria Dias Ferreira
rcaap.rightsrestrictedAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublication424b7c16-b769-4618-8c1c-58b29b36e81a
relation.isAuthorOfPublicatione30010dd-0512-4da6-a0c9-910b47f18b4f
relation.isAuthorOfPublication.latestForDiscovery424b7c16-b769-4618-8c1c-58b29b36e81a
relation.isProjectOfPublicationbfc45dda-8ea4-41eb-a77f-052f571a7454
relation.isProjectOfPublication107fd526-919a-44a3-b09a-2b512380cd23
relation.isProjectOfPublication.latestForDiscoverybfc45dda-8ea4-41eb-a77f-052f571a7454

Files

Original bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
2019-oliveira_et_al_-_JCLP_-_redes_neuronais-with_page_numbers.pdf
Size:
1.55 MB
Format:
Adobe Portable Document Format
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.97 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

Ambiente e Sustentabilidade | Artigos em revistas internacionais / Papers in international journals