Artigo

Water Quality Monitoring in Large Reservoirs Using Remote Sensing and Neural Networks

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 dis...

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Autor principal: RIBEIRO, Hebe Morganne Campos
Outros Autores: ALMEIDA, Arthur da Costa, ROCHA, Brigida Ramati Pereira da, KRUSCHE, Alex vladimir
Grau: Artigo
Idioma: por
Publicado em: Universidade Federal do Pará 2019
Assuntos:
water quality
Remote sensing
Artificial neural
Acesso em linha: http://repositorio.ufpa.br/jspui/handle/2011/12103
id ir-2011-12103
recordtype dspace
spelling ir-2011-121032019-12-04T15:49:15Z Water Quality Monitoring in Large Reservoirs Using Remote Sensing and Neural Networks RIBEIRO, Hebe Morganne Campos ALMEIDA, Arthur da Costa ROCHA, Brigida Ramati Pereira da KRUSCHE, Alex vladimir water quality Remote sensing Artificial neural 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. ALMEIDA, A. C. Universidade Federal do Pará 2019-11-29T16:39:19Z 2019-11-29T16:39:19Z 2018-09 Artigo de Periódico 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 http://repositorio.ufpa.br/jspui/handle/2011/12103 10.1109/TLA.2008.4839111 por IEEE LATIN AMERICA TRANSACTIONS Acesso Aberto application/pdf Universidade Federal do Pará Brasil UFPA Disponível na internet via correio eletrônico: riufpabc@ufpa.br
institution Repositório Institucional - Universidade Federal do Pará
collection RI-UFPA
language por
topic water quality
Remote sensing
Artificial neural
spellingShingle water quality
Remote sensing
Artificial neural
RIBEIRO, Hebe Morganne Campos
Water Quality Monitoring in Large Reservoirs Using Remote Sensing and Neural Networks
topic_facet water quality
Remote sensing
Artificial neural
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.
format Artigo
author RIBEIRO, Hebe Morganne Campos
author2 ALMEIDA, Arthur da Costa
ROCHA, Brigida Ramati Pereira da
KRUSCHE, Alex vladimir
author2Str ALMEIDA, Arthur da Costa
ROCHA, Brigida Ramati Pereira da
KRUSCHE, Alex vladimir
title Water Quality Monitoring in Large Reservoirs Using Remote Sensing and Neural Networks
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
publisher Universidade Federal do Pará
publishDate 2019
url http://repositorio.ufpa.br/jspui/handle/2011/12103
_version_ 1787148272173842432
score 11.675088

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