Classification of Basic Sanitation Services for Asset Valuation Using Random Forest
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | por |
Título da fonte: | Revista de Engenharia e Pesquisa Aplicada |
Texto Completo: | http://revistas.poli.br/index.php/repa/article/view/2148 |
Resumo: | In a regulatory agency's tariff review process, data consistency is of fundamental importance for better assertiveness. For this analysis, a large part of the highly relevant data is not informed, which leads to a manual process by the analysts responsible for the review. Aiming to assist the work, a case study was carried out with a qualitative and quantitative approach of the data aiming at extracting relevant information from a database made available with sewage and water supply assets, classification algorithms based on Machine Learning were implemented and validated. As a result, a Random Forest model capable of classifying the type of service in which the assets are inserted was developed, reaching an accuracy of approximately 80%. Thus, this work makes it possible to predict part of the missing information in reviews, which will reduce the agents' analysis time, in addition to reducing possible human errors in the process as a whole. |
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Classification of Basic Sanitation Services for Asset Valuation Using Random ForestClassificação dos Serviços de Saneamento Básico para Valoração de Ativos Utilizando Random Forest In a regulatory agency's tariff review process, data consistency is of fundamental importance for better assertiveness. For this analysis, a large part of the highly relevant data is not informed, which leads to a manual process by the analysts responsible for the review. Aiming to assist the work, a case study was carried out with a qualitative and quantitative approach of the data aiming at extracting relevant information from a database made available with sewage and water supply assets, classification algorithms based on Machine Learning were implemented and validated. As a result, a Random Forest model capable of classifying the type of service in which the assets are inserted was developed, reaching an accuracy of approximately 80%. Thus, this work makes it possible to predict part of the missing information in reviews, which will reduce the agents' analysis time, in addition to reducing possible human errors in the process as a whole. No processo de revisão tarifária de uma agência reguladora a consistência dos dados é de fundamental importância para uma melhor assertividade. Para esta análise, grande parte dos dados de suma relevância não são informados, o que leva a um processo manual dos analistas responsáveis pela revisão. Visando auxiliar o trabalho, foi realizado um estudo de caso com abordagem qualitativa e quantitativa dos dados visando a extração de informações relevantes a partir de uma base disponibilizada com ativos de esgoto e de abastecimento hídrico, algoritmos de classificação baseado em Aprendizado de Máquina foram implementados e validados. Como resultado, um modelo de Random Forest capaz de classificar o tipo de serviço no qual os ativos estão inseridos foi desenvolvido, atingindo uma acurácia de aproximadamente 80%. Deste modo, o presente trabalho viabiliza predizer parte das informações faltantes nas revisões, o que diminuirá o tempo de análise dos agentes, além de reduzir os possíveis erros humanos no processo como um todo.Escola Politécnica de Pernambuco2021-11-20info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdftext/htmlhttp://revistas.poli.br/index.php/repa/article/view/214810.25286/repa.v6i5.2148Journal of Engineering and Applied Research; Vol 6 No 5 (2021): Edição Especial em Ciência de Dados e Analytics; 90-99Revista de Engenharia e Pesquisa Aplicada; v. 6 n. 5 (2021): Edição Especial em Ciência de Dados e Analytics; 90-992525-425110.25286/repa.v6i5reponame:Revista de Engenharia e Pesquisa Aplicadainstname:Universidade Federal de Pernambuco (UFPE)instacron:UFPEporhttp://revistas.poli.br/index.php/repa/article/view/2148/790http://revistas.poli.br/index.php/repa/article/view/2148/791Copyright (c) 2021 Álysson de Sá Soares, Isabela de Oliveira Bulhões, Victor Guilherme Ferreira Trajano, Vitor Felix Oliveira da Silva, Alexandre Magno Andrade Macielhttp://creativecommons.org/licenses/by-nc/4.0info:eu-repo/semantics/openAccessSoares., Álysson de SáBulhões, Isabela de OliveiraTrajano, Victor Guilherme Ferreirada Silva, Vitor Felix OliveiraMaciel, Alexandre Magno Andrade2021-11-29T11:50:00Zoai:ojs.poli.br:article/2148Revistahttp://revistas.poli.br/index.php/repaONGhttp://revistas.poli.br/index.php/repa/oai||repa@poli.br2525-42512525-4251opendoar:2021-11-29T11:50Revista de Engenharia e Pesquisa Aplicada - Universidade Federal de Pernambuco (UFPE)false |
dc.title.none.fl_str_mv |
Classification of Basic Sanitation Services for Asset Valuation Using Random Forest Classificação dos Serviços de Saneamento Básico para Valoração de Ativos Utilizando Random Forest |
title |
Classification of Basic Sanitation Services for Asset Valuation Using Random Forest |
spellingShingle |
Classification of Basic Sanitation Services for Asset Valuation Using Random Forest Soares., Álysson de Sá |
title_short |
Classification of Basic Sanitation Services for Asset Valuation Using Random Forest |
title_full |
Classification of Basic Sanitation Services for Asset Valuation Using Random Forest |
title_fullStr |
Classification of Basic Sanitation Services for Asset Valuation Using Random Forest |
title_full_unstemmed |
Classification of Basic Sanitation Services for Asset Valuation Using Random Forest |
title_sort |
Classification of Basic Sanitation Services for Asset Valuation Using Random Forest |
author |
Soares., Álysson de Sá |
author_facet |
Soares., Álysson de Sá Bulhões, Isabela de Oliveira Trajano, Victor Guilherme Ferreira da Silva, Vitor Felix Oliveira Maciel, Alexandre Magno Andrade |
author_role |
author |
author2 |
Bulhões, Isabela de Oliveira Trajano, Victor Guilherme Ferreira da Silva, Vitor Felix Oliveira Maciel, Alexandre Magno Andrade |
author2_role |
author author author author |
dc.contributor.author.fl_str_mv |
Soares., Álysson de Sá Bulhões, Isabela de Oliveira Trajano, Victor Guilherme Ferreira da Silva, Vitor Felix Oliveira Maciel, Alexandre Magno Andrade |
description |
In a regulatory agency's tariff review process, data consistency is of fundamental importance for better assertiveness. For this analysis, a large part of the highly relevant data is not informed, which leads to a manual process by the analysts responsible for the review. Aiming to assist the work, a case study was carried out with a qualitative and quantitative approach of the data aiming at extracting relevant information from a database made available with sewage and water supply assets, classification algorithms based on Machine Learning were implemented and validated. As a result, a Random Forest model capable of classifying the type of service in which the assets are inserted was developed, reaching an accuracy of approximately 80%. Thus, this work makes it possible to predict part of the missing information in reviews, which will reduce the agents' analysis time, in addition to reducing possible human errors in the process as a whole. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-11-20 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://revistas.poli.br/index.php/repa/article/view/2148 10.25286/repa.v6i5.2148 |
url |
http://revistas.poli.br/index.php/repa/article/view/2148 |
identifier_str_mv |
10.25286/repa.v6i5.2148 |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.relation.none.fl_str_mv |
http://revistas.poli.br/index.php/repa/article/view/2148/790 http://revistas.poli.br/index.php/repa/article/view/2148/791 |
dc.rights.driver.fl_str_mv |
http://creativecommons.org/licenses/by-nc/4.0 info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
http://creativecommons.org/licenses/by-nc/4.0 |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf text/html |
dc.publisher.none.fl_str_mv |
Escola Politécnica de Pernambuco |
publisher.none.fl_str_mv |
Escola Politécnica de Pernambuco |
dc.source.none.fl_str_mv |
Journal of Engineering and Applied Research; Vol 6 No 5 (2021): Edição Especial em Ciência de Dados e Analytics; 90-99 Revista de Engenharia e Pesquisa Aplicada; v. 6 n. 5 (2021): Edição Especial em Ciência de Dados e Analytics; 90-99 2525-4251 10.25286/repa.v6i5 reponame:Revista de Engenharia e Pesquisa Aplicada instname:Universidade Federal de Pernambuco (UFPE) instacron:UFPE |
instname_str |
Universidade Federal de Pernambuco (UFPE) |
instacron_str |
UFPE |
institution |
UFPE |
reponame_str |
Revista de Engenharia e Pesquisa Aplicada |
collection |
Revista de Engenharia e Pesquisa Aplicada |
repository.name.fl_str_mv |
Revista de Engenharia e Pesquisa Aplicada - Universidade Federal de Pernambuco (UFPE) |
repository.mail.fl_str_mv |
||repa@poli.br |
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1798036000415416320 |