Tidal forecasting using RNN in Bahia Blanca estuary, Argentina
- Autores
- Pierini, Jorge Omar; Gomez, Eduardo Alberto
- Año de publicación
- 2009
- Idioma
- inglés
- Tipo de recurso
- artículo
- Estado
- versión publicada
- Descripción
- In recent years, the availability of accurate ocean tide models has become increasingly important, as tides are the main contributor to disposal and movement of sediments, tracers and pollutants, and also due to a wide range of offshore applications in engineering, environmental observations, exploration and oceanography. Tides can be conventionally predicted by harmonic analysis, which is the superposition of many sinusoidal constituents with amplitudes and frequencies determined by a local analysis of the measured tide. However, accurate predictions of tide levels could not be obtained without a large number of tide measurements by the harmonic method. An application of the back-propagation artifcial neural network using long-term and short-term measuring data is presented in this paper. On site tidal level data at Ingeniero White harbor in the inner part of Bahia Blanca estuary, Argentina, will be used to test the performance of the present model. Comparison with conventional harmonic methods indicates that the back-propagation neural network model also predicts accurately the long-term tidal levels.
Fil: Pierini, Jorge Omar. Comision Nacional de Investigacion Cientifica y Tecnologica; Chile. Universidad Nacional del Sur; Argentina
Fil: Gomez, Eduardo Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto Argentino de Oceanografía. Universidad Nacional del Sur. Instituto Argentino de Oceanografía; Argentina. Universidad Tecnológica Nacional; Argentina - Materia
-
Redes neuronales
Harmonic analysis
sea level
Prediction - Nivel de accesibilidad
- acceso abierto
- Condiciones de uso
- https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
- Repositorio
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/27855
Ver los metadatos del registro completo
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Tidal forecasting using RNN in Bahia Blanca estuary, ArgentinaPierini, Jorge OmarGomez, Eduardo AlbertoRedes neuronalesHarmonic analysissea levelPredictionhttps://purl.org/becyt/ford/1.5https://purl.org/becyt/ford/1In recent years, the availability of accurate ocean tide models has become increasingly important, as tides are the main contributor to disposal and movement of sediments, tracers and pollutants, and also due to a wide range of offshore applications in engineering, environmental observations, exploration and oceanography. Tides can be conventionally predicted by harmonic analysis, which is the superposition of many sinusoidal constituents with amplitudes and frequencies determined by a local analysis of the measured tide. However, accurate predictions of tide levels could not be obtained without a large number of tide measurements by the harmonic method. An application of the back-propagation artifcial neural network using long-term and short-term measuring data is presented in this paper. On site tidal level data at Ingeniero White harbor in the inner part of Bahia Blanca estuary, Argentina, will be used to test the performance of the present model. Comparison with conventional harmonic methods indicates that the back-propagation neural network model also predicts accurately the long-term tidal levels.Fil: Pierini, Jorge Omar. Comision Nacional de Investigacion Cientifica y Tecnologica; Chile. Universidad Nacional del Sur; ArgentinaFil: Gomez, Eduardo Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto Argentino de Oceanografía. Universidad Nacional del Sur. Instituto Argentino de Oceanografía; Argentina. Universidad Tecnológica Nacional; ArgentinaInterciencia2009-12info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/27855Pierini, Jorge Omar; Gomez, Eduardo Alberto; Tidal forecasting using RNN in Bahia Blanca estuary, Argentina; Interciencia; Interciencia; 34; 12; 12-2009; 851-8560378-1844CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/http://www.redalyc.org/pdf/339/33913151003.pdfinfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:41:39Zoai:ri.conicet.gov.ar:11336/27855instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 10:41:39.738CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse |
dc.title.none.fl_str_mv |
Tidal forecasting using RNN in Bahia Blanca estuary, Argentina |
title |
Tidal forecasting using RNN in Bahia Blanca estuary, Argentina |
spellingShingle |
Tidal forecasting using RNN in Bahia Blanca estuary, Argentina Pierini, Jorge Omar Redes neuronales Harmonic analysis sea level Prediction |
title_short |
Tidal forecasting using RNN in Bahia Blanca estuary, Argentina |
title_full |
Tidal forecasting using RNN in Bahia Blanca estuary, Argentina |
title_fullStr |
Tidal forecasting using RNN in Bahia Blanca estuary, Argentina |
title_full_unstemmed |
Tidal forecasting using RNN in Bahia Blanca estuary, Argentina |
title_sort |
Tidal forecasting using RNN in Bahia Blanca estuary, Argentina |
dc.creator.none.fl_str_mv |
Pierini, Jorge Omar Gomez, Eduardo Alberto |
author |
Pierini, Jorge Omar |
author_facet |
Pierini, Jorge Omar Gomez, Eduardo Alberto |
author_role |
author |
author2 |
Gomez, Eduardo Alberto |
author2_role |
author |
dc.subject.none.fl_str_mv |
Redes neuronales Harmonic analysis sea level Prediction |
topic |
Redes neuronales Harmonic analysis sea level Prediction |
purl_subject.fl_str_mv |
https://purl.org/becyt/ford/1.5 https://purl.org/becyt/ford/1 |
dc.description.none.fl_txt_mv |
In recent years, the availability of accurate ocean tide models has become increasingly important, as tides are the main contributor to disposal and movement of sediments, tracers and pollutants, and also due to a wide range of offshore applications in engineering, environmental observations, exploration and oceanography. Tides can be conventionally predicted by harmonic analysis, which is the superposition of many sinusoidal constituents with amplitudes and frequencies determined by a local analysis of the measured tide. However, accurate predictions of tide levels could not be obtained without a large number of tide measurements by the harmonic method. An application of the back-propagation artifcial neural network using long-term and short-term measuring data is presented in this paper. On site tidal level data at Ingeniero White harbor in the inner part of Bahia Blanca estuary, Argentina, will be used to test the performance of the present model. Comparison with conventional harmonic methods indicates that the back-propagation neural network model also predicts accurately the long-term tidal levels. Fil: Pierini, Jorge Omar. Comision Nacional de Investigacion Cientifica y Tecnologica; Chile. Universidad Nacional del Sur; Argentina Fil: Gomez, Eduardo Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto Argentino de Oceanografía. Universidad Nacional del Sur. Instituto Argentino de Oceanografía; Argentina. Universidad Tecnológica Nacional; Argentina |
description |
In recent years, the availability of accurate ocean tide models has become increasingly important, as tides are the main contributor to disposal and movement of sediments, tracers and pollutants, and also due to a wide range of offshore applications in engineering, environmental observations, exploration and oceanography. Tides can be conventionally predicted by harmonic analysis, which is the superposition of many sinusoidal constituents with amplitudes and frequencies determined by a local analysis of the measured tide. However, accurate predictions of tide levels could not be obtained without a large number of tide measurements by the harmonic method. An application of the back-propagation artifcial neural network using long-term and short-term measuring data is presented in this paper. On site tidal level data at Ingeniero White harbor in the inner part of Bahia Blanca estuary, Argentina, will be used to test the performance of the present model. Comparison with conventional harmonic methods indicates that the back-propagation neural network model also predicts accurately the long-term tidal levels. |
publishDate |
2009 |
dc.date.none.fl_str_mv |
2009-12 |
dc.type.none.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion http://purl.org/coar/resource_type/c_6501 info:ar-repo/semantics/articulo |
format |
article |
status_str |
publishedVersion |
dc.identifier.none.fl_str_mv |
http://hdl.handle.net/11336/27855 Pierini, Jorge Omar; Gomez, Eduardo Alberto; Tidal forecasting using RNN in Bahia Blanca estuary, Argentina; Interciencia; Interciencia; 34; 12; 12-2009; 851-856 0378-1844 CONICET Digital CONICET |
url |
http://hdl.handle.net/11336/27855 |
identifier_str_mv |
Pierini, Jorge Omar; Gomez, Eduardo Alberto; Tidal forecasting using RNN in Bahia Blanca estuary, Argentina; Interciencia; Interciencia; 34; 12; 12-2009; 851-856 0378-1844 CONICET Digital CONICET |
dc.language.none.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
info:eu-repo/semantics/altIdentifier/url/http://www.redalyc.org/pdf/339/33913151003.pdf |
dc.rights.none.fl_str_mv |
info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by-nc-sa/2.5/ar/ |
eu_rights_str_mv |
openAccess |
rights_invalid_str_mv |
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/ |
dc.format.none.fl_str_mv |
application/pdf application/pdf |
dc.publisher.none.fl_str_mv |
Interciencia |
publisher.none.fl_str_mv |
Interciencia |
dc.source.none.fl_str_mv |
reponame:CONICET Digital (CONICET) instname:Consejo Nacional de Investigaciones Científicas y Técnicas |
reponame_str |
CONICET Digital (CONICET) |
collection |
CONICET Digital (CONICET) |
instname_str |
Consejo Nacional de Investigaciones Científicas y Técnicas |
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CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas |
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dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar |
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13.070432 |