Assessing the naturalization degree of water in the Doce River through Grade of Membership (GoM)
Autor(a) principal: | |
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Data de Publicação: | 2015 |
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Tipo de documento: | Artigo |
Idioma: | por |
Título da fonte: | Revista Espinhaço |
Texto Completo: | https://revistas.ufvjm.edu.br/revista-espinhaco/article/view/69 |
Resumo: | Water quality is an extremely important topic to many scientific fields and also to the environmental management. Therefore, working with new paths to address the water quality is as challenging as relevant. The regular approach to the theme is the WQI, a worldwide-recognized model that integrates nine parameters. Although, the hardness of the WQI lead to gaps of interpretation. Assuming that the fuzzy logic can be an in-depth tool to reach the complexity of water quality, this work brings a methodological proposal to use the Grade of Membership method to measure the naturalization of water. This algorithm can indicate how closer any water sample is to a given pristine pattern. It was used 16 years of physical, chemical and biological parameters of water, sampled by trimester in 99 stations along the Doce River watershed. The results show that all samples are far from their pristine condition. Besides, almost all cases have some grade of membership to the three classes (good, medium and bad). The model was successfully applied, promoting a classification of the degree of naturalization of water. Furthermore, the fuzzy logic allows discussing the data beyond the classes, pointing the farther parameters to its pristine condition and guiding to a more efficient water and environmental management. |
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Assessing the naturalization degree of water in the Doce River through Grade of Membership (GoM)Aplicação do método Grade of Membership na classificação do grau de naturalização das águas na bacia do Rio Docequalidade da águanaturalizaçãoGoMclassificaçãowater qualitynaturalizationGoMclassificationWater quality is an extremely important topic to many scientific fields and also to the environmental management. Therefore, working with new paths to address the water quality is as challenging as relevant. The regular approach to the theme is the WQI, a worldwide-recognized model that integrates nine parameters. Although, the hardness of the WQI lead to gaps of interpretation. Assuming that the fuzzy logic can be an in-depth tool to reach the complexity of water quality, this work brings a methodological proposal to use the Grade of Membership method to measure the naturalization of water. This algorithm can indicate how closer any water sample is to a given pristine pattern. It was used 16 years of physical, chemical and biological parameters of water, sampled by trimester in 99 stations along the Doce River watershed. The results show that all samples are far from their pristine condition. Besides, almost all cases have some grade of membership to the three classes (good, medium and bad). The model was successfully applied, promoting a classification of the degree of naturalization of water. Furthermore, the fuzzy logic allows discussing the data beyond the classes, pointing the farther parameters to its pristine condition and guiding to a more efficient water and environmental management.A qualidade da água é um tema de grande importância para diversos campos da ciência e, também, para a gestão ambiental. Assim, trabalhar com novos caminhos para discutir a qualidade da água é tão desafiador quanto relevante. A abordagem comum para a temática é o IQA, um modelo reconhecido internacionalmente e amplamente replicado. Todavia, a rigidez do IQA leva a lacunas em sua interpretação. Métodos estatísticos baseados na lógica nebulosa oferecem ferramentas robustas capazes de captar a complexidade do tema. Diante disso, esse trabalho traz uma proposta metodológica de utilização do método Grade of Membership para avaliar a naturalização das águas. Esse algoritmo pode indicar o quão próximo uma amostra de água está de uma dada condição pristina. Foram utilizados dezesseis anos de dados físicos, químicos e biológicos de águas fluviais, amostradas trimestralmente em 99 estações ao longo da bacia do Rio Doce. Os resultados mostram que todas as amostras verificadas estão distantes de sua condição pristina. Além disso, praticamente todas possuem graus de inserção nas três classes criadas (bom, médio e ruim). Ademais, a lógica nebulosa viabiliza a discussão dos dados para além das classes definidas, apontando os parâmetros mais distantes de sua condição pristina, fornecendo subsídios para iniciativas de planejamento e gestão do meio ambiente e dos recursos hídricos.UFVJM2015-12-04info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionArtigo avaliado pelos Paresapplication/pdfhttps://revistas.ufvjm.edu.br/revista-espinhaco/article/view/6910.5281/zenodo.3962610Revista Espinhaço ; Revista Espinhaço #72317-0611reponame:Revista Espinhaçoinstname:Universidade Federal dos Vales do Jequitinhonha e Mucuri (UFVJM)instacron:UFVJMporhttps://revistas.ufvjm.edu.br/revista-espinhaco/article/view/69/73Copyright (c) 2022 Revista Espinhaço https://creativecommons.org/licenses/by-nc-nd/4.0info:eu-repo/semantics/openAccessMoura, Mirella Nazareth deFelippe, Miguel Fernandes2022-07-22T18:44:49Zoai:ojs.pkp.sfu.ca:article/69Revistahttps://revistaespinhaco.com/index.php/revista/indexPUBhttps://revistas.ufvjm.edu.br/revista-espinhaco/oairevista.espinhaco@gmail.com || doug.sathler@gmail.com2317-06112317-0611opendoar:2022-07-22T18:44:49Revista Espinhaço - Universidade Federal dos Vales do Jequitinhonha e Mucuri (UFVJM)false |
dc.title.none.fl_str_mv |
Assessing the naturalization degree of water in the Doce River through Grade of Membership (GoM) Aplicação do método Grade of Membership na classificação do grau de naturalização das águas na bacia do Rio Doce |
title |
Assessing the naturalization degree of water in the Doce River through Grade of Membership (GoM) |
spellingShingle |
Assessing the naturalization degree of water in the Doce River through Grade of Membership (GoM) Moura, Mirella Nazareth de qualidade da água naturalização GoM classificação water quality naturalization GoM classification |
title_short |
Assessing the naturalization degree of water in the Doce River through Grade of Membership (GoM) |
title_full |
Assessing the naturalization degree of water in the Doce River through Grade of Membership (GoM) |
title_fullStr |
Assessing the naturalization degree of water in the Doce River through Grade of Membership (GoM) |
title_full_unstemmed |
Assessing the naturalization degree of water in the Doce River through Grade of Membership (GoM) |
title_sort |
Assessing the naturalization degree of water in the Doce River through Grade of Membership (GoM) |
author |
Moura, Mirella Nazareth de |
author_facet |
Moura, Mirella Nazareth de Felippe, Miguel Fernandes |
author_role |
author |
author2 |
Felippe, Miguel Fernandes |
author2_role |
author |
dc.contributor.author.fl_str_mv |
Moura, Mirella Nazareth de Felippe, Miguel Fernandes |
dc.subject.por.fl_str_mv |
qualidade da água naturalização GoM classificação water quality naturalization GoM classification |
topic |
qualidade da água naturalização GoM classificação water quality naturalization GoM classification |
description |
Water quality is an extremely important topic to many scientific fields and also to the environmental management. Therefore, working with new paths to address the water quality is as challenging as relevant. The regular approach to the theme is the WQI, a worldwide-recognized model that integrates nine parameters. Although, the hardness of the WQI lead to gaps of interpretation. Assuming that the fuzzy logic can be an in-depth tool to reach the complexity of water quality, this work brings a methodological proposal to use the Grade of Membership method to measure the naturalization of water. This algorithm can indicate how closer any water sample is to a given pristine pattern. It was used 16 years of physical, chemical and biological parameters of water, sampled by trimester in 99 stations along the Doce River watershed. The results show that all samples are far from their pristine condition. Besides, almost all cases have some grade of membership to the three classes (good, medium and bad). The model was successfully applied, promoting a classification of the degree of naturalization of water. Furthermore, the fuzzy logic allows discussing the data beyond the classes, pointing the farther parameters to its pristine condition and guiding to a more efficient water and environmental management. |
publishDate |
2015 |
dc.date.none.fl_str_mv |
2015-12-04 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Artigo avaliado pelos Pares |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://revistas.ufvjm.edu.br/revista-espinhaco/article/view/69 10.5281/zenodo.3962610 |
url |
https://revistas.ufvjm.edu.br/revista-espinhaco/article/view/69 |
identifier_str_mv |
10.5281/zenodo.3962610 |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.relation.none.fl_str_mv |
https://revistas.ufvjm.edu.br/revista-espinhaco/article/view/69/73 |
dc.rights.driver.fl_str_mv |
Copyright (c) 2022 Revista Espinhaço https://creativecommons.org/licenses/by-nc-nd/4.0 info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Copyright (c) 2022 Revista Espinhaço https://creativecommons.org/licenses/by-nc-nd/4.0 |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
UFVJM |
publisher.none.fl_str_mv |
UFVJM |
dc.source.none.fl_str_mv |
Revista Espinhaço ; Revista Espinhaço #7 2317-0611 reponame:Revista Espinhaço instname:Universidade Federal dos Vales do Jequitinhonha e Mucuri (UFVJM) instacron:UFVJM |
instname_str |
Universidade Federal dos Vales do Jequitinhonha e Mucuri (UFVJM) |
instacron_str |
UFVJM |
institution |
UFVJM |
reponame_str |
Revista Espinhaço |
collection |
Revista Espinhaço |
repository.name.fl_str_mv |
Revista Espinhaço - Universidade Federal dos Vales do Jequitinhonha e Mucuri (UFVJM) |
repository.mail.fl_str_mv |
revista.espinhaco@gmail.com || doug.sathler@gmail.com |
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