STREAMFLOW FORECASTING FOR THE DAM ORÓS/CE FROM HYDROMETEOROLOGICAL DATA USING PERCEPTRONS
The modeling of seasonal and interannual streamflow forecasting at northeastern Brazil represents a great relevance problem to the use and management of water resources; which demands greater prediction ability models. This is still a difficult task to solve due to the seasonal and interannual clima...
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Sociedade Brasileira de Meteorologia
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ftdoajarticles:oai:doaj.org/article:d7e6e4cd9d114c7b945edab371ac1a09 2023-05-15T17:33:28+02:00 STREAMFLOW FORECASTING FOR THE DAM ORÓS/CE FROM HYDROMETEOROLOGICAL DATA USING PERCEPTRONS Carla Beatriz Costa de Araújo Silvrano Adonias Dantas Neto Francisco de Assis Souza Filho 2015-03-01T00:00:00Z https://doi.org/10.1590/0102-778620140048 https://doaj.org/article/d7e6e4cd9d114c7b945edab371ac1a09 EN PT eng por Sociedade Brasileira de Meteorologia http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0102-77862015000100037&lng=en&tlng=en https://doaj.org/toc/1982-4351 1982-4351 doi:10.1590/0102-778620140048 https://doaj.org/article/d7e6e4cd9d114c7b945edab371ac1a09 Revista Brasileira de Meteorologia, Vol 30, Iss 1, Pp 37-46 (2015) Redes neurais artificiais Previsão de vazões Açude Orós Meteorology. Climatology QC851-999 article 2015 ftdoajarticles https://doi.org/10.1590/0102-778620140048 2022-12-30T23:16:13Z The modeling of seasonal and interannual streamflow forecasting at northeastern Brazil represents a great relevance problem to the use and management of water resources; which demands greater prediction ability models. This is still a difficult task to solve due to the seasonal and interannual climate variability at the semi-arid region. This work presents the artificial neural networks (ANN) as an alternative for modeling the seasonal to interannual climate prediction,. For the development of this task the hydropraphic Oros weir Basin was chosen due to its importance as water resources in the State of Ceara. According to recent studies, the temperatures of the North Atlantic, South Atlantic and equatorial Pacific can be satisfactorily as predictors for the Northeast climate. The proposed model predicts, in July, the next rainy season (January to June) river flow regime. This time frame is of great relevance for the allocation of water resources. Among the studied models, those using the average temperature anomalies of April, May and June preceding the predicted year as input data showed the highest Nash-Suttcliffe efficiency (0.80). Article in Journal/Newspaper North Atlantic Directory of Open Access Journals: DOAJ Articles June River ENVELOPE(-64.000,-64.000,66.767,66.767) Nash ENVELOPE(-62.350,-62.350,-74.233,-74.233) Pacific Weir ENVELOPE(177.167,177.167,-84.983,-84.983) Revista Brasileira de Meteorologia 30 1 37 46 |
institution |
Open Polar |
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Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English Portuguese |
topic |
Redes neurais artificiais Previsão de vazões Açude Orós Meteorology. Climatology QC851-999 |
spellingShingle |
Redes neurais artificiais Previsão de vazões Açude Orós Meteorology. Climatology QC851-999 Carla Beatriz Costa de Araújo Silvrano Adonias Dantas Neto Francisco de Assis Souza Filho STREAMFLOW FORECASTING FOR THE DAM ORÓS/CE FROM HYDROMETEOROLOGICAL DATA USING PERCEPTRONS |
topic_facet |
Redes neurais artificiais Previsão de vazões Açude Orós Meteorology. Climatology QC851-999 |
description |
The modeling of seasonal and interannual streamflow forecasting at northeastern Brazil represents a great relevance problem to the use and management of water resources; which demands greater prediction ability models. This is still a difficult task to solve due to the seasonal and interannual climate variability at the semi-arid region. This work presents the artificial neural networks (ANN) as an alternative for modeling the seasonal to interannual climate prediction,. For the development of this task the hydropraphic Oros weir Basin was chosen due to its importance as water resources in the State of Ceara. According to recent studies, the temperatures of the North Atlantic, South Atlantic and equatorial Pacific can be satisfactorily as predictors for the Northeast climate. The proposed model predicts, in July, the next rainy season (January to June) river flow regime. This time frame is of great relevance for the allocation of water resources. Among the studied models, those using the average temperature anomalies of April, May and June preceding the predicted year as input data showed the highest Nash-Suttcliffe efficiency (0.80). |
format |
Article in Journal/Newspaper |
author |
Carla Beatriz Costa de Araújo Silvrano Adonias Dantas Neto Francisco de Assis Souza Filho |
author_facet |
Carla Beatriz Costa de Araújo Silvrano Adonias Dantas Neto Francisco de Assis Souza Filho |
author_sort |
Carla Beatriz Costa de Araújo |
title |
STREAMFLOW FORECASTING FOR THE DAM ORÓS/CE FROM HYDROMETEOROLOGICAL DATA USING PERCEPTRONS |
title_short |
STREAMFLOW FORECASTING FOR THE DAM ORÓS/CE FROM HYDROMETEOROLOGICAL DATA USING PERCEPTRONS |
title_full |
STREAMFLOW FORECASTING FOR THE DAM ORÓS/CE FROM HYDROMETEOROLOGICAL DATA USING PERCEPTRONS |
title_fullStr |
STREAMFLOW FORECASTING FOR THE DAM ORÓS/CE FROM HYDROMETEOROLOGICAL DATA USING PERCEPTRONS |
title_full_unstemmed |
STREAMFLOW FORECASTING FOR THE DAM ORÓS/CE FROM HYDROMETEOROLOGICAL DATA USING PERCEPTRONS |
title_sort |
streamflow forecasting for the dam orós/ce from hydrometeorological data using perceptrons |
publisher |
Sociedade Brasileira de Meteorologia |
publishDate |
2015 |
url |
https://doi.org/10.1590/0102-778620140048 https://doaj.org/article/d7e6e4cd9d114c7b945edab371ac1a09 |
long_lat |
ENVELOPE(-64.000,-64.000,66.767,66.767) ENVELOPE(-62.350,-62.350,-74.233,-74.233) ENVELOPE(177.167,177.167,-84.983,-84.983) |
geographic |
June River Nash Pacific Weir |
geographic_facet |
June River Nash Pacific Weir |
genre |
North Atlantic |
genre_facet |
North Atlantic |
op_source |
Revista Brasileira de Meteorologia, Vol 30, Iss 1, Pp 37-46 (2015) |
op_relation |
http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0102-77862015000100037&lng=en&tlng=en https://doaj.org/toc/1982-4351 1982-4351 doi:10.1590/0102-778620140048 https://doaj.org/article/d7e6e4cd9d114c7b945edab371ac1a09 |
op_doi |
https://doi.org/10.1590/0102-778620140048 |
container_title |
Revista Brasileira de Meteorologia |
container_volume |
30 |
container_issue |
1 |
container_start_page |
37 |
op_container_end_page |
46 |
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1766131980064260096 |