Application of ANNs for the modeling of streamflow, sediment transport, and erosion rate of a high-altitude river system in Western Himalaya, Uttarakhand
Autor(a) principal: | |
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Data de Publicação: | 2022 |
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=S2318-03312022000100220 |
Resumo: | ABSTRACT The estimation of stream discharge is an essential component of planning and decision-making. It is highly correlated with many development activities involving water resources. The study of transportation of sediments in the rivers will help us to develop policies and plans for soil conservation, flood control, irrigation, navigation, and aquatic biodiversity problems. Using data-driven models such as Artificial Neural Networks (ANNs), modeling of streamflow and sediment transport is frequently adopted due to their applicability and problem-solving ability. This study has used three training algorithms such as Scaled Conjugate Gradient (SCG), Bayesian Regularization (BR), and Levenberg-Marquardt (LM) to simulate the streamflow and Suspended Sediments Concentration (SSC). After optimizing the best training algorithm based on the model efficiency parameters, L-M based-ANN model has been used to predict streamflow for two years and the modeling of suspended sediments was validated with the help of observed data. The result shows that the simulated results tracked the streamflow as well as SSC with the desired accuracy based on the model efficiency parameters such as coefficient of Determination (R2), Nash Sutcliffe Efficiency (NSE), Root Mean Square Error (RMSE), and Root Mean Square Deviation (RMSD). The study's outcomes reveal that in the streamflow the concentration of suspended sediments is significantly affected by the base rock material, glaciers covered by debris, and moraine-laden ice. The transportation of the sediments is high in the Alaknanda basin as compared to the other basins and the previous studies. This might happen due to the severe anthropogenic activities in the surrounding basin. |
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Application of ANNs for the modeling of streamflow, sediment transport, and erosion rate of a high-altitude river system in Western Himalaya, UttarakhandArtificial Neural Networks (ANNs)Data-drivenDecision-makersSediment transportStreamflow modelingTraining algorithmsABSTRACT The estimation of stream discharge is an essential component of planning and decision-making. It is highly correlated with many development activities involving water resources. The study of transportation of sediments in the rivers will help us to develop policies and plans for soil conservation, flood control, irrigation, navigation, and aquatic biodiversity problems. Using data-driven models such as Artificial Neural Networks (ANNs), modeling of streamflow and sediment transport is frequently adopted due to their applicability and problem-solving ability. This study has used three training algorithms such as Scaled Conjugate Gradient (SCG), Bayesian Regularization (BR), and Levenberg-Marquardt (LM) to simulate the streamflow and Suspended Sediments Concentration (SSC). After optimizing the best training algorithm based on the model efficiency parameters, L-M based-ANN model has been used to predict streamflow for two years and the modeling of suspended sediments was validated with the help of observed data. The result shows that the simulated results tracked the streamflow as well as SSC with the desired accuracy based on the model efficiency parameters such as coefficient of Determination (R2), Nash Sutcliffe Efficiency (NSE), Root Mean Square Error (RMSE), and Root Mean Square Deviation (RMSD). The study's outcomes reveal that in the streamflow the concentration of suspended sediments is significantly affected by the base rock material, glaciers covered by debris, and moraine-laden ice. The transportation of the sediments is high in the Alaknanda basin as compared to the other basins and the previous studies. This might happen due to the severe anthropogenic activities in the surrounding basin.Associação Brasileira de Recursos Hídricos2022-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S2318-03312022000100220RBRH v.27 2022reponame:RBRH (Online)instname:Associação Brasileira de Recursos Hídricos (ABRH)instacron:ABRH10.1590/2318-0331.272220220045info:eu-repo/semantics/openAccessRautela,Kuldeep SinghKumar,DilipGandhi,Bandaru Goutham RajeevKumar,AjayDubey,Amit Kumareng2022-10-06T00:00:00Zoai:scielo:S2318-03312022000100220Revistahttps://www.scielo.br/j/rbrh/https://old.scielo.br/oai/scielo-oai.php||rbrh@abrh.org.br2318-03311414-381Xopendoar:2022-10-06T00:00RBRH (Online) - Associação Brasileira de Recursos Hídricos (ABRH)false |
dc.title.none.fl_str_mv |
Application of ANNs for the modeling of streamflow, sediment transport, and erosion rate of a high-altitude river system in Western Himalaya, Uttarakhand |
title |
Application of ANNs for the modeling of streamflow, sediment transport, and erosion rate of a high-altitude river system in Western Himalaya, Uttarakhand |
spellingShingle |
Application of ANNs for the modeling of streamflow, sediment transport, and erosion rate of a high-altitude river system in Western Himalaya, Uttarakhand Rautela,Kuldeep Singh Artificial Neural Networks (ANNs) Data-driven Decision-makers Sediment transport Streamflow modeling Training algorithms |
title_short |
Application of ANNs for the modeling of streamflow, sediment transport, and erosion rate of a high-altitude river system in Western Himalaya, Uttarakhand |
title_full |
Application of ANNs for the modeling of streamflow, sediment transport, and erosion rate of a high-altitude river system in Western Himalaya, Uttarakhand |
title_fullStr |
Application of ANNs for the modeling of streamflow, sediment transport, and erosion rate of a high-altitude river system in Western Himalaya, Uttarakhand |
title_full_unstemmed |
Application of ANNs for the modeling of streamflow, sediment transport, and erosion rate of a high-altitude river system in Western Himalaya, Uttarakhand |
title_sort |
Application of ANNs for the modeling of streamflow, sediment transport, and erosion rate of a high-altitude river system in Western Himalaya, Uttarakhand |
author |
Rautela,Kuldeep Singh |
author_facet |
Rautela,Kuldeep Singh Kumar,Dilip Gandhi,Bandaru Goutham Rajeev Kumar,Ajay Dubey,Amit Kumar |
author_role |
author |
author2 |
Kumar,Dilip Gandhi,Bandaru Goutham Rajeev Kumar,Ajay Dubey,Amit Kumar |
author2_role |
author author author author |
dc.contributor.author.fl_str_mv |
Rautela,Kuldeep Singh Kumar,Dilip Gandhi,Bandaru Goutham Rajeev Kumar,Ajay Dubey,Amit Kumar |
dc.subject.por.fl_str_mv |
Artificial Neural Networks (ANNs) Data-driven Decision-makers Sediment transport Streamflow modeling Training algorithms |
topic |
Artificial Neural Networks (ANNs) Data-driven Decision-makers Sediment transport Streamflow modeling Training algorithms |
description |
ABSTRACT The estimation of stream discharge is an essential component of planning and decision-making. It is highly correlated with many development activities involving water resources. The study of transportation of sediments in the rivers will help us to develop policies and plans for soil conservation, flood control, irrigation, navigation, and aquatic biodiversity problems. Using data-driven models such as Artificial Neural Networks (ANNs), modeling of streamflow and sediment transport is frequently adopted due to their applicability and problem-solving ability. This study has used three training algorithms such as Scaled Conjugate Gradient (SCG), Bayesian Regularization (BR), and Levenberg-Marquardt (LM) to simulate the streamflow and Suspended Sediments Concentration (SSC). After optimizing the best training algorithm based on the model efficiency parameters, L-M based-ANN model has been used to predict streamflow for two years and the modeling of suspended sediments was validated with the help of observed data. The result shows that the simulated results tracked the streamflow as well as SSC with the desired accuracy based on the model efficiency parameters such as coefficient of Determination (R2), Nash Sutcliffe Efficiency (NSE), Root Mean Square Error (RMSE), and Root Mean Square Deviation (RMSD). The study's outcomes reveal that in the streamflow the concentration of suspended sediments is significantly affected by the base rock material, glaciers covered by debris, and moraine-laden ice. The transportation of the sediments is high in the Alaknanda basin as compared to the other basins and the previous studies. This might happen due to the severe anthropogenic activities in the surrounding basin. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-01-01 |
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=S2318-03312022000100220 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S2318-03312022000100220 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/2318-0331.272220220045 |
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.27 2022 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 |
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1754734702338506752 |