Water tariff forecasting models applied to municipal and private companies in the south and southeast regions of Brazil
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1007/s10661-020-08387-y http://hdl.handle.net/11449/200599 |
Resumo: | This 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. |
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Repositório Institucional da UNESP |
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Water tariff forecasting models applied to municipal and private companies in the south and southeast regions of BrazilArtificial neural networkSNIS, forecast modelsWater tariffThis 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.Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Departamento de Engenharia Civil Faculdade de Engenharia de Ilha Solteira - UNESPDepartamento 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, PampulhaDepartamento de Matemática da Faculdade de Engenharia de Ilha Solteira, Avenida Brasil, n° 56 – CentroDepartamento de Engenharia Civil Faculdade de Engenharia de Ilha Solteira - UNESPUniversidade Estadual Paulista (Unesp)Universidade Federal de Minas Gerais (UFMG)Bezerra, [UNESP]de Oliveira Bezerra, Alberto Guilherme [UNESP]Libânio, MarceloLopes, Mara Lúcia Martins2020-12-12T02:10:54Z2020-12-12T02:10:54Z2020-06-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://dx.doi.org/10.1007/s10661-020-08387-yEnvironmental Monitoring and Assessment, v. 192, n. 7, 2020.1573-29590167-6369http://hdl.handle.net/11449/20059910.1007/s10661-020-08387-y2-s2.0-85086380180Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengEnvironmental Monitoring and Assessmentinfo:eu-repo/semantics/openAccess2024-07-04T18:16:11Zoai:repositorio.unesp.br:11449/200599Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T21:44:49.439755Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Water tariff forecasting models applied to municipal and private companies in the south and southeast regions of Brazil |
title |
Water tariff forecasting models applied to municipal and private companies in the south and southeast regions of Brazil |
spellingShingle |
Water tariff forecasting models applied to municipal and private companies in the south and southeast regions of Brazil Bezerra, [UNESP] Artificial neural network SNIS, forecast models Water tariff |
title_short |
Water tariff forecasting models applied to municipal and private companies in the south and southeast regions of Brazil |
title_full |
Water tariff forecasting models applied to municipal and private companies in the south and southeast regions of Brazil |
title_fullStr |
Water tariff forecasting models applied to municipal and private companies in the south and southeast regions of Brazil |
title_full_unstemmed |
Water tariff forecasting models applied to municipal and private companies in the south and southeast regions of Brazil |
title_sort |
Water tariff forecasting models applied to municipal and private companies in the south and southeast regions of Brazil |
author |
Bezerra, [UNESP] |
author_facet |
Bezerra, [UNESP] de Oliveira Bezerra, Alberto Guilherme [UNESP] Libânio, Marcelo Lopes, Mara Lúcia Martins |
author_role |
author |
author2 |
de Oliveira Bezerra, Alberto Guilherme [UNESP] Libânio, Marcelo Lopes, Mara Lúcia Martins |
author2_role |
author author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (Unesp) Universidade Federal de Minas Gerais (UFMG) |
dc.contributor.author.fl_str_mv |
Bezerra, [UNESP] de Oliveira Bezerra, Alberto Guilherme [UNESP] Libânio, Marcelo Lopes, Mara Lúcia Martins |
dc.subject.por.fl_str_mv |
Artificial neural network SNIS, forecast models Water tariff |
topic |
Artificial neural network SNIS, forecast models Water tariff |
description |
This 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. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020-12-12T02:10:54Z 2020-12-12T02:10:54Z 2020-06-01 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1007/s10661-020-08387-y Environmental Monitoring and Assessment, v. 192, n. 7, 2020. 1573-2959 0167-6369 http://hdl.handle.net/11449/200599 10.1007/s10661-020-08387-y 2-s2.0-85086380180 |
url |
http://dx.doi.org/10.1007/s10661-020-08387-y http://hdl.handle.net/11449/200599 |
identifier_str_mv |
Environmental Monitoring and Assessment, v. 192, n. 7, 2020. 1573-2959 0167-6369 10.1007/s10661-020-08387-y 2-s2.0-85086380180 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Environmental Monitoring and Assessment |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.source.none.fl_str_mv |
Scopus reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1808129353776627712 |