Using artificial neural networks to determine sediment concentration at limit of deposition in storm sewers
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | RBRH (Online) |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S2318-03312021000100225 |
Resumo: | ABSTRACT The concept of sediment transport at the limit of deposition in storm sewers represents one operational condition that avoid deposition of sediments maintaining the discharge capacity of the pipes. In this study, this condition was analyzed applying one Artificial Neural Network Multilayer Perceptron (ANN-MLP) model to predict the volumetric concentration at the limit of deposition, using 544 experimental data from literature. It was evaluated different input variables combinations and model configurations, showing the sensitivity of the model with these changes. Through this study, it was demonstrated that the proposed model outperforms the existing equations, leading to more assertive predictions in the determination of volumetric concentrations at the limit of deposition, resulting in values of R2 = 0.92, Mean Absolute Percentage Error (MAPE) = 35.09 % and Mean Average Error (MAE) = 59.84 ppm. With the performed analysis, the study selects one equation to be used for extrapolations when determining the volumetric concentration at the limit of deposition in storm sewers. The selected equation is superior due to its theoretical basis. This work includes one more concept to a better methodology in obtaining the conditions of the flow at the limit of deposition. |
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RBRH (Online) |
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Using artificial neural networks to determine sediment concentration at limit of deposition in storm sewersLimit of depositionSediment transportArtificial neural networksABSTRACT The concept of sediment transport at the limit of deposition in storm sewers represents one operational condition that avoid deposition of sediments maintaining the discharge capacity of the pipes. In this study, this condition was analyzed applying one Artificial Neural Network Multilayer Perceptron (ANN-MLP) model to predict the volumetric concentration at the limit of deposition, using 544 experimental data from literature. It was evaluated different input variables combinations and model configurations, showing the sensitivity of the model with these changes. Through this study, it was demonstrated that the proposed model outperforms the existing equations, leading to more assertive predictions in the determination of volumetric concentrations at the limit of deposition, resulting in values of R2 = 0.92, Mean Absolute Percentage Error (MAPE) = 35.09 % and Mean Average Error (MAE) = 59.84 ppm. With the performed analysis, the study selects one equation to be used for extrapolations when determining the volumetric concentration at the limit of deposition in storm sewers. The selected equation is superior due to its theoretical basis. This work includes one more concept to a better methodology in obtaining the conditions of the flow at the limit of deposition.Associação Brasileira de Recursos Hídricos2021-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S2318-03312021000100225RBRH v.26 2021reponame:RBRH (Online)instname:Associação Brasileira de Recursos Hídricos (ABRH)instacron:ABRH10.1590/2318-0331.262120210030info:eu-repo/semantics/openAccessRomero,AdhemarOta,José Junjieng2021-08-06T00:00:00Zoai:scielo:S2318-03312021000100225Revistahttps://www.scielo.br/j/rbrh/https://old.scielo.br/oai/scielo-oai.php||rbrh@abrh.org.br2318-03311414-381Xopendoar:2021-08-06T00:00RBRH (Online) - Associação Brasileira de Recursos Hídricos (ABRH)false |
dc.title.none.fl_str_mv |
Using artificial neural networks to determine sediment concentration at limit of deposition in storm sewers |
title |
Using artificial neural networks to determine sediment concentration at limit of deposition in storm sewers |
spellingShingle |
Using artificial neural networks to determine sediment concentration at limit of deposition in storm sewers Romero,Adhemar Limit of deposition Sediment transport Artificial neural networks |
title_short |
Using artificial neural networks to determine sediment concentration at limit of deposition in storm sewers |
title_full |
Using artificial neural networks to determine sediment concentration at limit of deposition in storm sewers |
title_fullStr |
Using artificial neural networks to determine sediment concentration at limit of deposition in storm sewers |
title_full_unstemmed |
Using artificial neural networks to determine sediment concentration at limit of deposition in storm sewers |
title_sort |
Using artificial neural networks to determine sediment concentration at limit of deposition in storm sewers |
author |
Romero,Adhemar |
author_facet |
Romero,Adhemar Ota,José Junji |
author_role |
author |
author2 |
Ota,José Junji |
author2_role |
author |
dc.contributor.author.fl_str_mv |
Romero,Adhemar Ota,José Junji |
dc.subject.por.fl_str_mv |
Limit of deposition Sediment transport Artificial neural networks |
topic |
Limit of deposition Sediment transport Artificial neural networks |
description |
ABSTRACT The concept of sediment transport at the limit of deposition in storm sewers represents one operational condition that avoid deposition of sediments maintaining the discharge capacity of the pipes. In this study, this condition was analyzed applying one Artificial Neural Network Multilayer Perceptron (ANN-MLP) model to predict the volumetric concentration at the limit of deposition, using 544 experimental data from literature. It was evaluated different input variables combinations and model configurations, showing the sensitivity of the model with these changes. Through this study, it was demonstrated that the proposed model outperforms the existing equations, leading to more assertive predictions in the determination of volumetric concentrations at the limit of deposition, resulting in values of R2 = 0.92, Mean Absolute Percentage Error (MAPE) = 35.09 % and Mean Average Error (MAE) = 59.84 ppm. With the performed analysis, the study selects one equation to be used for extrapolations when determining the volumetric concentration at the limit of deposition in storm sewers. The selected equation is superior due to its theoretical basis. This work includes one more concept to a better methodology in obtaining the conditions of the flow at the limit of deposition. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-01-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S2318-03312021000100225 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S2318-03312021000100225 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/2318-0331.262120210030 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
text/html |
dc.publisher.none.fl_str_mv |
Associação Brasileira de Recursos Hídricos |
publisher.none.fl_str_mv |
Associação Brasileira de Recursos Hídricos |
dc.source.none.fl_str_mv |
RBRH v.26 2021 reponame:RBRH (Online) instname:Associação Brasileira de Recursos Hídricos (ABRH) instacron:ABRH |
instname_str |
Associação Brasileira de Recursos Hídricos (ABRH) |
instacron_str |
ABRH |
institution |
ABRH |
reponame_str |
RBRH (Online) |
collection |
RBRH (Online) |
repository.name.fl_str_mv |
RBRH (Online) - Associação Brasileira de Recursos Hídricos (ABRH) |
repository.mail.fl_str_mv |
||rbrh@abrh.org.br |
_version_ |
1754734702283980800 |