Please use this identifier to cite or link to this item: http://repositorio.ufpa.br/jspui/handle/2011/12103
metadata.dc.type: Artigo de Periódico
Issue Date: Sep-2018
metadata.dc.creator: RIBEIRO, Hebe Morganne Campos
ALMEIDA, Arthur da Costa
ROCHA, Brigida Ramati Pereira da
KRUSCHE, Alex vladimir
metadata.dc.description.affiliation: ALMEIDA, A. C. Universidade Federal do Pará
Title: Water Quality Monitoring in Large Reservoirs Using Remote Sensing and Neural Networks
Citation: 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:.
Abstract: 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.
Keywords: water quality
Remote sensing
Artificial neural
Series/Report no.: IEEE LATIN AMERICA TRANSACTIONS
ISSN: 1548-0992
metadata.dc.publisher.country: Brasil
Publisher: Universidade Federal do Pará
metadata.dc.publisher.initials: UFPA
metadata.dc.rights: Acesso Aberto
metadata.dc.source.uri: Disponível na internet via correio eletrônico: riufpabc@ufpa.br
metadata.dc.identifier.doi: 10.1109/TLA.2008.4839111
Appears in Collections:Artigos Científicos - ICB

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