Using neural networks for estimation of aquifer dynamical behavior

Detalhes bibliográficos
Autor(a) principal: da Silva, I. N.
Data de Publicação: 2000
Outros Autores: Saggioro, N. J., Cagnon, J. A.
Tipo de documento: Artigo de conferência
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.1109/IJCNN.2000.859397
http://hdl.handle.net/11449/8893
Resumo: The systems of water distribution from groundwater wells can be monitored using the changes observed on its dynamical behavior. In this paper, artificial neural networks are used to estimate the depth of the dynamical water level of groundwater wells in relation to water flow, operation time and rest time. Simulation results are presented to demonstrate the validity of the proposed approach. These results have shown that artificial neural networks can be effectively used for the identification and estimation of parameters related to systems of water distribution.
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spelling Using neural networks for estimation of aquifer dynamical behaviorThe systems of water distribution from groundwater wells can be monitored using the changes observed on its dynamical behavior. In this paper, artificial neural networks are used to estimate the depth of the dynamical water level of groundwater wells in relation to water flow, operation time and rest time. Simulation results are presented to demonstrate the validity of the proposed approach. These results have shown that artificial neural networks can be effectively used for the identification and estimation of parameters related to systems of water distribution.Univ São Paulo, UNESP, FE, DEE,Sch Engn,Dept Elect Engn, Bauru, SP, BrazilUniv São Paulo, UNESP, FE, DEE,Sch Engn,Dept Elect Engn, Bauru, SP, BrazilInstitute of Electrical and Electronics Engineers (IEEE), Computer SocUniversidade Estadual Paulista (Unesp)da Silva, I. N.Saggioro, N. J.Cagnon, J. A.2014-05-20T13:27:13Z2014-05-20T13:27:13Z2000-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject203-207http://dx.doi.org/10.1109/IJCNN.2000.859397Ijcnn 2000: Proceedings of the IEEE-inns-enns International Joint Conference on Neural Networks, Vol Vi. Los Alamitos: IEEE Computer Soc, p. 203-207, 2000.1098-7576http://hdl.handle.net/11449/889310.1109/IJCNN.2000.859397WOS:0000892406000340783942619645974Web of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengIjcnn 2000: Proceedings of the IEEE-inns-enns International Joint Conference on Neural Networks, Vol Viinfo:eu-repo/semantics/openAccess2024-06-28T13:34:35Zoai:repositorio.unesp.br:11449/8893Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T17:50:59.115366Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv Using neural networks for estimation of aquifer dynamical behavior
title Using neural networks for estimation of aquifer dynamical behavior
spellingShingle Using neural networks for estimation of aquifer dynamical behavior
da Silva, I. N.
title_short Using neural networks for estimation of aquifer dynamical behavior
title_full Using neural networks for estimation of aquifer dynamical behavior
title_fullStr Using neural networks for estimation of aquifer dynamical behavior
title_full_unstemmed Using neural networks for estimation of aquifer dynamical behavior
title_sort Using neural networks for estimation of aquifer dynamical behavior
author da Silva, I. N.
author_facet da Silva, I. N.
Saggioro, N. J.
Cagnon, J. A.
author_role author
author2 Saggioro, N. J.
Cagnon, J. A.
author2_role author
author
dc.contributor.none.fl_str_mv Universidade Estadual Paulista (Unesp)
dc.contributor.author.fl_str_mv da Silva, I. N.
Saggioro, N. J.
Cagnon, J. A.
description The systems of water distribution from groundwater wells can be monitored using the changes observed on its dynamical behavior. In this paper, artificial neural networks are used to estimate the depth of the dynamical water level of groundwater wells in relation to water flow, operation time and rest time. Simulation results are presented to demonstrate the validity of the proposed approach. These results have shown that artificial neural networks can be effectively used for the identification and estimation of parameters related to systems of water distribution.
publishDate 2000
dc.date.none.fl_str_mv 2000-01-01
2014-05-20T13:27:13Z
2014-05-20T13:27:13Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/conferenceObject
format conferenceObject
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://dx.doi.org/10.1109/IJCNN.2000.859397
Ijcnn 2000: Proceedings of the IEEE-inns-enns International Joint Conference on Neural Networks, Vol Vi. Los Alamitos: IEEE Computer Soc, p. 203-207, 2000.
1098-7576
http://hdl.handle.net/11449/8893
10.1109/IJCNN.2000.859397
WOS:000089240600034
0783942619645974
url http://dx.doi.org/10.1109/IJCNN.2000.859397
http://hdl.handle.net/11449/8893
identifier_str_mv Ijcnn 2000: Proceedings of the IEEE-inns-enns International Joint Conference on Neural Networks, Vol Vi. Los Alamitos: IEEE Computer Soc, p. 203-207, 2000.
1098-7576
10.1109/IJCNN.2000.859397
WOS:000089240600034
0783942619645974
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Ijcnn 2000: Proceedings of the IEEE-inns-enns International Joint Conference on Neural Networks, Vol Vi
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 203-207
dc.publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers (IEEE), Computer Soc
publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers (IEEE), Computer Soc
dc.source.none.fl_str_mv Web of Science
reponame:Repositório Institucional da UNESP
instname:Universidade Estadual Paulista (UNESP)
instacron:UNESP
instname_str Universidade Estadual Paulista (UNESP)
instacron_str UNESP
institution UNESP
reponame_str Repositório Institucional da UNESP
collection Repositório Institucional da UNESP
repository.name.fl_str_mv Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)
repository.mail.fl_str_mv
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