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Comparison of artificial intelligence algorithms to estimate sustainability indicators

2020-12Journal article Restricted access
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dc.contributor.authorBienvenido-Huertas, David
dc.contributor.authorFarinha, Fátima
dc.contributor.authorOliveira, Miguel José
dc.contributor.authorDa Silva, Elisa Maria De Jesus
dc.contributor.authorLança, Rui
dc.date.accessioned2021-06-18T16:25:45Z
dc.date.available2021-06-18T16:25:45Z
dc.date.issued2020-12
dc.description.abstractthe monitoring of sustainability indicators allows behavioural tendencies of a region to be controlled, so that adequate policies could be established in advance for a sustainable development. However, some data could be missed in the monitoring of these indicators, thus making the establishment of sustainability policies difficult. This paper therefore analyses the possibility to forecast the sustainability indicators of a region by using four different artificial intelligent algorithms: linear regression, multilayer perceptron, random forest, and M5P. the study area selected was the Algarve region in Portugal, and 180 monitored indicators were analysed between 2011 and 2017. the results showed that M5P is the most appropriate algorithm to estimate sustainability indicators. M5P was the algorithm obtaining the best estimations in a greater number of indicators. Nevertheless, the results showed that MP5 was not the best option for all indicators, since in some of them, the use of other algorithms obtained better results, thus reflecting the need of an individual previous study of each indicator. With these algorithms, it is possible for public bodies and institutions to evaluate the sustainable development of the region and to have reliable information to take corrective measures when needed, thus contributing to a more sustainable future.
dc.description.sponsorshipOperational Program CRESC ALGARVE 2020 [ALG-01-0246-FEDER-027503]
dc.description.versioninfo:eu-repo/semantics/publishedVersion
dc.identifier.doi10.1016/j.scs.2020.102430
dc.identifier.issn2210-6707
dc.identifier.urihttp://hdl.handle.net/10400.1/15662
dc.language.isoeng
dc.peerreviewedyes
dc.publisherElsevier
dc.subjectArtificial intelligence
dc.subjectSustainability indicators
dc.subjectOBSERVE platform
dc.subjectData mining
dc.subjectMonitoring process
dc.subject.otherConstruction & Building Technology
dc.subject.otherScience & Technology
dc.subject.otherEnergy & Fuels
dc.titleComparison of artificial intelligence algorithms to estimate sustainability indicators
dc.typejournal article
dspace.entity.typePublication
oaire.citation.startPage102430
oaire.citation.titleSustainable Cities and Society
oaire.citation.volume63
person.familyNameFarinha
person.familyNameOliveira
person.familyNameSilva
person.familyNameLança
person.givenNameFátima
person.givenNameMiguel José
person.givenNameElisa Maria de Jesus da
person.givenNameRui
person.identifierR-000-B2P
person.identifierR-001-Y1B
person.identifierR-001-Y2N
person.identifier.ciencia-id5C19-57E8-932F
person.identifier.ciencia-idE012-0342-0756
person.identifier.ciencia-id8317-3C2A-D74A
person.identifier.ciencia-idCD11-F4F2-95A3
person.identifier.orcid0000-0002-2056-6453
person.identifier.orcid0000-0002-3042-0802
person.identifier.orcid0000-0003-0037-2798
person.identifier.orcid0000-0002-7753-3767
person.identifier.ridM-4880-2017
person.identifier.ridT-2877-2017
person.identifier.ridA-9743-2016
person.identifier.ridE-5613-2017
person.identifier.scopus-author-id8965941500
person.identifier.scopus-author-id57205447350
person.identifier.scopus-author-id57193905012
person.identifier.scopus-author-id55436772300
rcaap.rightsrestrictedAccess
rcaap.typearticle
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