Prediction of the longitudinal dispersion coefficient for small watercourses
Autor(a) principal: | |
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Data de Publicação: | 2017 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Acta scientiarum. Technology (Online) |
Texto Completo: | http://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/29397 |
Resumo: | Longitudinal dispersion coefficient (DL) is considered an essential physical parameter to water quality modeling in rivers. Therefore, the estimation of this parameter with high accuracy guarantees the reliability of the results of a water quality model. In this study, the observed values of longitudinal dispersion coefficient are determined for natural streams (with discharge less than 2.84 m3s-1), based on sets of measured data from stimulus-response tests using sodium chloride as a tracer. Additionally, a semi-empirical equation for prediction of DL is derived using dimensional analysis and multiple linear regression technique. The performance of the produced equation was compared to five empirical prediction equations of DL selected from literature. It presented correlation coefficient r2 = 0.87, suggesting that this equation is suitable for the estimation of DL in streams. It also presented better results for predicting the DL than the five equations from literature, showing an accuracy of 71%. |
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Acta scientiarum. Technology (Online) |
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Prediction of the longitudinal dispersion coefficient for small watercourseslongitudinal dispersion coefficientsmall watercoursessodium chloride tracer.Engenharia Hidráulica Longitudinal dispersion coefficient (DL) is considered an essential physical parameter to water quality modeling in rivers. Therefore, the estimation of this parameter with high accuracy guarantees the reliability of the results of a water quality model. In this study, the observed values of longitudinal dispersion coefficient are determined for natural streams (with discharge less than 2.84 m3s-1), based on sets of measured data from stimulus-response tests using sodium chloride as a tracer. Additionally, a semi-empirical equation for prediction of DL is derived using dimensional analysis and multiple linear regression technique. The performance of the produced equation was compared to five empirical prediction equations of DL selected from literature. It presented correlation coefficient r2 = 0.87, suggesting that this equation is suitable for the estimation of DL in streams. It also presented better results for predicting the DL than the five equations from literature, showing an accuracy of 71%. Universidade Estadual De Maringá2017-07-06info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionmodelagem física e matemáticaapplication/pdfhttp://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/2939710.4025/actascitechnol.v39i3.29397Acta Scientiarum. Technology; Vol 39 No 3 (2017); 291-299Acta Scientiarum. Technology; v. 39 n. 3 (2017); 291-2991806-25631807-8664reponame:Acta scientiarum. Technology (Online)instname:Universidade Estadual de Maringá (UEM)instacron:UEMenghttp://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/29397/pdfCopyright (c) 2017 Acta Scientiarum. Technologyinfo:eu-repo/semantics/openAccessOliveira, Vanessa Vaz deMateus, Marcos ViníciusGonçalves, Julio Cesar de Souza InácioUtsumi, Alex GarcezGiorgetti, Marcius Fantozzi2017-07-14T10:09:05Zoai:periodicos.uem.br/ojs:article/29397Revistahttps://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/indexPUBhttps://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/oai||actatech@uem.br1807-86641806-2563opendoar:2017-07-14T10:09:05Acta scientiarum. Technology (Online) - Universidade Estadual de Maringá (UEM)false |
dc.title.none.fl_str_mv |
Prediction of the longitudinal dispersion coefficient for small watercourses |
title |
Prediction of the longitudinal dispersion coefficient for small watercourses |
spellingShingle |
Prediction of the longitudinal dispersion coefficient for small watercourses Oliveira, Vanessa Vaz de longitudinal dispersion coefficient small watercourses sodium chloride tracer. Engenharia Hidráulica |
title_short |
Prediction of the longitudinal dispersion coefficient for small watercourses |
title_full |
Prediction of the longitudinal dispersion coefficient for small watercourses |
title_fullStr |
Prediction of the longitudinal dispersion coefficient for small watercourses |
title_full_unstemmed |
Prediction of the longitudinal dispersion coefficient for small watercourses |
title_sort |
Prediction of the longitudinal dispersion coefficient for small watercourses |
author |
Oliveira, Vanessa Vaz de |
author_facet |
Oliveira, Vanessa Vaz de Mateus, Marcos Vinícius Gonçalves, Julio Cesar de Souza Inácio Utsumi, Alex Garcez Giorgetti, Marcius Fantozzi |
author_role |
author |
author2 |
Mateus, Marcos Vinícius Gonçalves, Julio Cesar de Souza Inácio Utsumi, Alex Garcez Giorgetti, Marcius Fantozzi |
author2_role |
author author author author |
dc.contributor.author.fl_str_mv |
Oliveira, Vanessa Vaz de Mateus, Marcos Vinícius Gonçalves, Julio Cesar de Souza Inácio Utsumi, Alex Garcez Giorgetti, Marcius Fantozzi |
dc.subject.por.fl_str_mv |
longitudinal dispersion coefficient small watercourses sodium chloride tracer. Engenharia Hidráulica |
topic |
longitudinal dispersion coefficient small watercourses sodium chloride tracer. Engenharia Hidráulica |
description |
Longitudinal dispersion coefficient (DL) is considered an essential physical parameter to water quality modeling in rivers. Therefore, the estimation of this parameter with high accuracy guarantees the reliability of the results of a water quality model. In this study, the observed values of longitudinal dispersion coefficient are determined for natural streams (with discharge less than 2.84 m3s-1), based on sets of measured data from stimulus-response tests using sodium chloride as a tracer. Additionally, a semi-empirical equation for prediction of DL is derived using dimensional analysis and multiple linear regression technique. The performance of the produced equation was compared to five empirical prediction equations of DL selected from literature. It presented correlation coefficient r2 = 0.87, suggesting that this equation is suitable for the estimation of DL in streams. It also presented better results for predicting the DL than the five equations from literature, showing an accuracy of 71%. |
publishDate |
2017 |
dc.date.none.fl_str_mv |
2017-07-06 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion modelagem física e matemática |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/29397 10.4025/actascitechnol.v39i3.29397 |
url |
http://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/29397 |
identifier_str_mv |
10.4025/actascitechnol.v39i3.29397 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
http://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/29397/pdf |
dc.rights.driver.fl_str_mv |
Copyright (c) 2017 Acta Scientiarum. Technology info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Copyright (c) 2017 Acta Scientiarum. Technology |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Universidade Estadual De Maringá |
publisher.none.fl_str_mv |
Universidade Estadual De Maringá |
dc.source.none.fl_str_mv |
Acta Scientiarum. Technology; Vol 39 No 3 (2017); 291-299 Acta Scientiarum. Technology; v. 39 n. 3 (2017); 291-299 1806-2563 1807-8664 reponame:Acta scientiarum. Technology (Online) instname:Universidade Estadual de Maringá (UEM) instacron:UEM |
instname_str |
Universidade Estadual de Maringá (UEM) |
instacron_str |
UEM |
institution |
UEM |
reponame_str |
Acta scientiarum. Technology (Online) |
collection |
Acta scientiarum. Technology (Online) |
repository.name.fl_str_mv |
Acta scientiarum. Technology (Online) - Universidade Estadual de Maringá (UEM) |
repository.mail.fl_str_mv |
||actatech@uem.br |
_version_ |
1799315335975796736 |