Water tariff forecasting models applied to municipal and private companies in the south and southeast regions of Brazil

dc.contributor.authorBezerra, [UNESP]
dc.contributor.authorde Oliveira Bezerra, Alberto Guilherme [UNESP]
dc.contributor.authorLibânio, Marcelo
dc.contributor.authorLopes, Mara Lúcia Martins
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.contributor.institutionUniversidade Federal de Minas Gerais (UFMG)
dc.date.accessioned2020-12-12T02:10:54Z
dc.date.available2020-12-12T02:10:54Z
dc.date.issued2020-06-01
dc.description.abstractThis paper has as a main goal to evaluate how models of the forecast will work with a group of variables that were selected based only on their correlation with the average tariff variation. Two forecast models are used, the first based on multiple linear regression techniques and the second based on the application of artificial neural networks (perceptron). We intend to use those models to reach the current water tariff based on the historic variation of the charge and the selected variables applied to municipal and private companies that operate water supply and wastewater systems in the South and Southeast regions of Brazil. The subsidiary data for the elaboration of the models were obtained through the National Sanitation Information System (SNIS). The obtained results indicated that the forecasting processes, in both models used, were able to forecast with high accuracy the fees, and guaranteed the maintenance of the surplus for the analyzed systems.en
dc.description.affiliationDepartamento de Engenharia Civil Faculdade de Engenharia de Ilha Solteira - UNESP
dc.description.affiliationDepartamento de Engenharia Hidráulica e Recursos Hídricos Universidade Federal de Minas Gerais Escola de Engenharia, Av. Antônio Carlos, 6627, Bloco I, Escola de Engenharia, Sala 4610, Pampulha
dc.description.affiliationDepartamento de Matemática da Faculdade de Engenharia de Ilha Solteira, Avenida Brasil, n° 56 – Centro
dc.description.affiliationUnespDepartamento de Engenharia Civil Faculdade de Engenharia de Ilha Solteira - UNESP
dc.description.sponsorshipCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
dc.identifierhttp://dx.doi.org/10.1007/s10661-020-08387-y
dc.identifier.citationEnvironmental Monitoring and Assessment, v. 192, n. 7, 2020.
dc.identifier.doi10.1007/s10661-020-08387-y
dc.identifier.issn1573-2959
dc.identifier.issn0167-6369
dc.identifier.scopus2-s2.0-85086380180
dc.identifier.urihttp://hdl.handle.net/11449/200599
dc.language.isoeng
dc.relation.ispartofEnvironmental Monitoring and Assessment
dc.sourceScopus
dc.subjectArtificial neural network
dc.subjectSNIS, forecast models
dc.subjectWater tariff
dc.titleWater tariff forecasting models applied to municipal and private companies in the south and southeast regions of Brazilen
dc.typeArtigo
unesp.author.orcid0000-0003-2682-9100[2]

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