Water Quality Monitoring in Large Reservoirs Using Remote Sensing and Neural Networks
Autor(a) principal: | |
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Data de Publicação: | 2018 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | por |
Título da fonte: | Repositório Institucional da UFPA |
Texto Completo: | http://repositorio.ufpa.br/jspui/handle/2011/12103 |
Resumo: | Water quality monitoring in lakes and reservoirs using water samples and laboratorial analysis is expensive and time consuming. The use of artificial neural networks to predict water quality using satellite images shows great potential to make this process faster and at lower costs. This article discusses an indirect method to estimate the concentration of pigments (chlorophyll-a), an optically active parameter in water quality. A model based on artificial neural networks, using radial base functions architecture, was developed to predict Tucurui’s Reservoir chlorophyll-a concentrations. As input to the neural networks spectral information from Landsat imagery was used, while pigment concentration were used as output information. To train and validate the model we used data from the years 1987, 1988, 1995, 1999, 2000 and 2004. The tested model showed a correlation coefficient of 0.92 for the estimation of pigment (chlorophyll-a) concentrations, indicating its applicability to predict this water quality parameter. |
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2019-11-29T16:39:19Z2019-11-29T16:39:19Z2018-09ALMEIDA, Arthur da Costa et al. Water Quality Monitoring in Large Reservoirs Using Remote Sensing and Neural Networks. IEEE Latin American Transactions, [S. l.], v. 6, n. 5, p. 419-423, Sept. 2018. DOI 10.1109/TLA.2008.4839111. Disponível em:. Acesso em:.1548-0992http://repositorio.ufpa.br/jspui/handle/2011/1210310.1109/TLA.2008.4839111Water quality monitoring in lakes and reservoirs using water samples and laboratorial analysis is expensive and time consuming. The use of artificial neural networks to predict water quality using satellite images shows great potential to make this process faster and at lower costs. This article discusses an indirect method to estimate the concentration of pigments (chlorophyll-a), an optically active parameter in water quality. A model based on artificial neural networks, using radial base functions architecture, was developed to predict Tucurui’s Reservoir chlorophyll-a concentrations. As input to the neural networks spectral information from Landsat imagery was used, while pigment concentration were used as output information. To train and validate the model we used data from the years 1987, 1988, 1995, 1999, 2000 and 2004. The tested model showed a correlation coefficient of 0.92 for the estimation of pigment (chlorophyll-a) concentrations, indicating its applicability to predict this water quality parameter.ALMEIDA, A. C. Universidade Federal do ParáporUniversidade Federal do ParáUFPABrasilIEEE LATIN AMERICA TRANSACTIONSDisponível na internet via correio eletrônico: riufpabc@ufpa.brreponame:Repositório Institucional da UFPAinstname:Universidade Federal do Pará (UFPA)instacron:UFPAArtificial neuralwater qualityRemote sensingWater Quality Monitoring in Large Reservoirs Using Remote Sensing and Neural Networksinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article65419423http://lattes.cnpq.br/2399134205919272http://lattes.cnpq.br/2014957882626187http://lattes.cnpq.br/9943372249006341http://lattes.cnpq.br/4787380927153288RIBEIRO, Hebe Morganne CamposALMEIDA, Arthur da CostaROCHA, Brigida Ramati Pereira daKRUSCHE, Alex vladimirinfo:eu-repo/semantics/openAccessORIGINALArticle_WaterQualityMonitoring.pdfArticle_WaterQualityMonitoring.pdfapplication/pdf771744http://repositorio.ufpa.br/oai/bitstream/2011/12103/1/Article_WaterQualityMonitoring.pdf750b634b1068bacb44edc42d2c17b609MD51CC-LICENSElicense_urllicense_urltext/plain; charset=utf-849http://repositorio.ufpa.br/oai/bitstream/2011/12103/2/license_url4afdbb8c545fd630ea7db775da747b2fMD52license_textlicense_texttext/html; charset=utf-80http://repositorio.ufpa.br/oai/bitstream/2011/12103/3/license_textd41d8cd98f00b204e9800998ecf8427eMD53license_rdflicense_rdfapplication/rdf+xml; charset=utf-80http://repositorio.ufpa.br/oai/bitstream/2011/12103/4/license_rdfd41d8cd98f00b204e9800998ecf8427eMD54LICENSElicense.txtlicense.txttext/plain; charset=utf-81899http://repositorio.ufpa.br/oai/bitstream/2011/12103/5/license.txt9d4d300cff78e8f375d89aab37134138MD55TEXTArticle_WaterQualityMonitoring.pdf.txtArticle_WaterQualityMonitoring.pdf.txtExtracted texttext/plain26999http://repositorio.ufpa.br/oai/bitstream/2011/12103/6/Article_WaterQualityMonitoring.pdf.txtb6f0e658f0d3fae418678a935a389679MD562011/121032019-12-04 12:49:15.667oai:repositorio.ufpa.br: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ório InstitucionalPUBhttp://repositorio.ufpa.br/oai/requestriufpabc@ufpa.bropendoar:21232019-12-04T15:49:15Repositório Institucional da UFPA - Universidade Federal do Pará (UFPA)false |
dc.title.en.fl_str_mv |
Water Quality Monitoring in Large Reservoirs Using Remote Sensing and Neural Networks |
title |
Water Quality Monitoring in Large Reservoirs Using Remote Sensing and Neural Networks |
spellingShingle |
Water Quality Monitoring in Large Reservoirs Using Remote Sensing and Neural Networks RIBEIRO, Hebe Morganne Campos Artificial neural water quality Remote sensing |
title_short |
Water Quality Monitoring in Large Reservoirs Using Remote Sensing and Neural Networks |
title_full |
Water Quality Monitoring in Large Reservoirs Using Remote Sensing and Neural Networks |
title_fullStr |
Water Quality Monitoring in Large Reservoirs Using Remote Sensing and Neural Networks |
title_full_unstemmed |
Water Quality Monitoring in Large Reservoirs Using Remote Sensing and Neural Networks |
title_sort |
Water Quality Monitoring in Large Reservoirs Using Remote Sensing and Neural Networks |
author |
RIBEIRO, Hebe Morganne Campos |
author_facet |
RIBEIRO, Hebe Morganne Campos ALMEIDA, Arthur da Costa ROCHA, Brigida Ramati Pereira da KRUSCHE, Alex vladimir |
author_role |
author |
author2 |
ALMEIDA, Arthur da Costa ROCHA, Brigida Ramati Pereira da KRUSCHE, Alex vladimir |
author2_role |
author author author |
dc.contributor.authorLattes.fl_str_mv |
http://lattes.cnpq.br/2399134205919272 http://lattes.cnpq.br/2014957882626187 http://lattes.cnpq.br/9943372249006341 http://lattes.cnpq.br/4787380927153288 |
dc.contributor.author.fl_str_mv |
RIBEIRO, Hebe Morganne Campos ALMEIDA, Arthur da Costa ROCHA, Brigida Ramati Pereira da KRUSCHE, Alex vladimir |
dc.subject.por.fl_str_mv |
Artificial neural |
topic |
Artificial neural water quality Remote sensing |
dc.subject.eng.fl_str_mv |
water quality Remote sensing |
description |
Water quality monitoring in lakes and reservoirs using water samples and laboratorial analysis is expensive and time consuming. The use of artificial neural networks to predict water quality using satellite images shows great potential to make this process faster and at lower costs. This article discusses an indirect method to estimate the concentration of pigments (chlorophyll-a), an optically active parameter in water quality. A model based on artificial neural networks, using radial base functions architecture, was developed to predict Tucurui’s Reservoir chlorophyll-a concentrations. As input to the neural networks spectral information from Landsat imagery was used, while pigment concentration were used as output information. To train and validate the model we used data from the years 1987, 1988, 1995, 1999, 2000 and 2004. The tested model showed a correlation coefficient of 0.92 for the estimation of pigment (chlorophyll-a) concentrations, indicating its applicability to predict this water quality parameter. |
publishDate |
2018 |
dc.date.issued.fl_str_mv |
2018-09 |
dc.date.accessioned.fl_str_mv |
2019-11-29T16:39:19Z |
dc.date.available.fl_str_mv |
2019-11-29T16:39:19Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.citation.fl_str_mv |
ALMEIDA, Arthur da Costa et al. Water Quality Monitoring in Large Reservoirs Using Remote Sensing and Neural Networks. IEEE Latin American Transactions, [S. l.], v. 6, n. 5, p. 419-423, Sept. 2018. DOI 10.1109/TLA.2008.4839111. Disponível em:. Acesso em:. |
dc.identifier.uri.fl_str_mv |
http://repositorio.ufpa.br/jspui/handle/2011/12103 |
dc.identifier.issn.pt_BR.fl_str_mv |
1548-0992 |
dc.identifier.doi.pt_BR.fl_str_mv |
10.1109/TLA.2008.4839111 |
identifier_str_mv |
ALMEIDA, Arthur da Costa et al. Water Quality Monitoring in Large Reservoirs Using Remote Sensing and Neural Networks. IEEE Latin American Transactions, [S. l.], v. 6, n. 5, p. 419-423, Sept. 2018. DOI 10.1109/TLA.2008.4839111. Disponível em:. Acesso em:. 1548-0992 10.1109/TLA.2008.4839111 |
url |
http://repositorio.ufpa.br/jspui/handle/2011/12103 |
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por |
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por |
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IEEE LATIN AMERICA TRANSACTIONS |
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info:eu-repo/semantics/openAccess |
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openAccess |
dc.publisher.none.fl_str_mv |
Universidade Federal do Pará |
dc.publisher.initials.fl_str_mv |
UFPA |
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Brasil |
publisher.none.fl_str_mv |
Universidade Federal do Pará |
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UFPA |
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Repositório Institucional da UFPA |
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Disponível na internet via correio eletrônico: riufpabc@ufpa.br |
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