MONTHLY RAINFALL FORECAST IN THE MUNICIPALITY OF BARRA MANSA/RJ USING DEEP LEARNING TIME SERIES TECHNIQUES
Autor(a) principal: | |
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Data de Publicação: | 2023 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | por eng |
Título da fonte: | Holos |
Texto Completo: | http://www2.ifrn.edu.br/ojs/index.php/HOLOS/article/view/16340 |
Resumo: | Precipitation forecasting is essential for sectors such as water resources management and urban planning. In this study, a deep learning model was developed to predict rainfall in Brazilian cities, focusing on the municipality of Barra Mansa, Rio de Janeiro. Four neural network architectures were tested: FCN, Resnet, ResCNN and InceptionTime. Among them, FCN stood out significantly, presenting the lowest error rates and the best overall adjustment. The study highlights the ability of deep learning, especially through the FCN (Fully Convolutional Network - Segmented) architecture, to make accurate predictions and uncover hidden rainfall patterns. Such discoveries have great potential to improve rainfall forecasting systems and assist in decision-making in areas that require accurate climate information. |
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MONTHLY RAINFALL FORECAST IN THE MUNICIPALITY OF BARRA MANSA/RJ USING DEEP LEARNING TIME SERIES TECHNIQUESPREVISÃO DE PRECIPITAÇÃO MENSAL NO MUNICÍPIO DE BARRA MANSA/RJ USANDO TÉCNICAS DE DEEP LEARNING TIME SERIESForecasting, precipitation, rainfall, deep learning, neural networksPrevisão, precipitação, chuvas, apredizagem profunda, redes neurais.Precipitation forecasting is essential for sectors such as water resources management and urban planning. In this study, a deep learning model was developed to predict rainfall in Brazilian cities, focusing on the municipality of Barra Mansa, Rio de Janeiro. Four neural network architectures were tested: FCN, Resnet, ResCNN and InceptionTime. Among them, FCN stood out significantly, presenting the lowest error rates and the best overall adjustment. The study highlights the ability of deep learning, especially through the FCN (Fully Convolutional Network - Segmented) architecture, to make accurate predictions and uncover hidden rainfall patterns. Such discoveries have great potential to improve rainfall forecasting systems and assist in decision-making in areas that require accurate climate information.A previsão de precipitações é essencial para setores como gestão de recursos hídricos e planejamento urbano. Neste estudo, foi desenvolvido um modelo de aprendizagem profunda (deep learning) para prever chuvas em cidades brasileiras, com foco no município de Barra Mansa, Rio de Janeiro. Foram testadas quatro arquiteturas de redes neurais: FCN, Resnet, ResCNN e InceptionTime. Dentre elas, a FCN se destacou significativamente, apresentando os menores índices de erro e o melhor ajuste global. O estudo evidencia a capacidade da aprendizagem profunda, especialmente através da arquitetura FCN, em fazer previsões precisas e desvendar padrões ocultos das chuvas. Tais descobertas possuem grande potencial para aprimorar sistemas de previsão de chuvas e auxiliar na tomada de decisões em áreas que necessitam de informações climáticas acuradas. Instituto Federal de Educação, Ciência e Tecnologia do Rio Grande do Norte2023-12-18info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionTextoinfo:eu-repo/semantics/otherapplication/pdfapplication/pdfhttp://www2.ifrn.edu.br/ojs/index.php/HOLOS/article/view/1634010.15628/holos.2023.16340HOLOS; v. 5 n. 39 (2023): v.5 (2023)1807-1600reponame:Holosinstname:Instituto Federal do Rio Grande do Norte (IFRN)instacron:IFRNporenghttp://www2.ifrn.edu.br/ojs/index.php/HOLOS/article/view/16340/3846http://www2.ifrn.edu.br/ojs/index.php/HOLOS/article/view/16340/3847Barra Mansa, 1940 - 2023Barra Mansa, 1940 - 2023https://creativecommons.org/licenses/by-nc-nd/4.0info:eu-repo/semantics/openAccessde Azevedo Silva, ViníciusMateus PeixotoSantos, Francisco Lledo2023-12-28T03:14:36Zoai:holos.ifrn.edu.br:article/16340Revistahttp://www2.ifrn.edu.br/ojs/index.php/HOLOSPUBhttp://www2.ifrn.edu.br/ojs/index.php/HOLOS/oaiholos@ifrn.edu.br||jyp.leite@ifrn.edu.br||propi@ifrn.edu.br1807-16001518-1634opendoar:2023-12-28T03:14:36Holos - Instituto Federal do Rio Grande do Norte (IFRN)false |
dc.title.none.fl_str_mv |
MONTHLY RAINFALL FORECAST IN THE MUNICIPALITY OF BARRA MANSA/RJ USING DEEP LEARNING TIME SERIES TECHNIQUES PREVISÃO DE PRECIPITAÇÃO MENSAL NO MUNICÍPIO DE BARRA MANSA/RJ USANDO TÉCNICAS DE DEEP LEARNING TIME SERIES |
title |
MONTHLY RAINFALL FORECAST IN THE MUNICIPALITY OF BARRA MANSA/RJ USING DEEP LEARNING TIME SERIES TECHNIQUES |
spellingShingle |
MONTHLY RAINFALL FORECAST IN THE MUNICIPALITY OF BARRA MANSA/RJ USING DEEP LEARNING TIME SERIES TECHNIQUES de Azevedo Silva, Vinícius Forecasting, precipitation, rainfall, deep learning, neural networks Previsão, precipitação, chuvas, apredizagem profunda, redes neurais. |
title_short |
MONTHLY RAINFALL FORECAST IN THE MUNICIPALITY OF BARRA MANSA/RJ USING DEEP LEARNING TIME SERIES TECHNIQUES |
title_full |
MONTHLY RAINFALL FORECAST IN THE MUNICIPALITY OF BARRA MANSA/RJ USING DEEP LEARNING TIME SERIES TECHNIQUES |
title_fullStr |
MONTHLY RAINFALL FORECAST IN THE MUNICIPALITY OF BARRA MANSA/RJ USING DEEP LEARNING TIME SERIES TECHNIQUES |
title_full_unstemmed |
MONTHLY RAINFALL FORECAST IN THE MUNICIPALITY OF BARRA MANSA/RJ USING DEEP LEARNING TIME SERIES TECHNIQUES |
title_sort |
MONTHLY RAINFALL FORECAST IN THE MUNICIPALITY OF BARRA MANSA/RJ USING DEEP LEARNING TIME SERIES TECHNIQUES |
author |
de Azevedo Silva, Vinícius |
author_facet |
de Azevedo Silva, Vinícius Mateus Peixoto Santos, Francisco Lledo |
author_role |
author |
author2 |
Mateus Peixoto Santos, Francisco Lledo |
author2_role |
author author |
dc.contributor.author.fl_str_mv |
de Azevedo Silva, Vinícius Mateus Peixoto Santos, Francisco Lledo |
dc.subject.por.fl_str_mv |
Forecasting, precipitation, rainfall, deep learning, neural networks Previsão, precipitação, chuvas, apredizagem profunda, redes neurais. |
topic |
Forecasting, precipitation, rainfall, deep learning, neural networks Previsão, precipitação, chuvas, apredizagem profunda, redes neurais. |
description |
Precipitation forecasting is essential for sectors such as water resources management and urban planning. In this study, a deep learning model was developed to predict rainfall in Brazilian cities, focusing on the municipality of Barra Mansa, Rio de Janeiro. Four neural network architectures were tested: FCN, Resnet, ResCNN and InceptionTime. Among them, FCN stood out significantly, presenting the lowest error rates and the best overall adjustment. The study highlights the ability of deep learning, especially through the FCN (Fully Convolutional Network - Segmented) architecture, to make accurate predictions and uncover hidden rainfall patterns. Such discoveries have great potential to improve rainfall forecasting systems and assist in decision-making in areas that require accurate climate information. |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023-12-18 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Texto info:eu-repo/semantics/other |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://www2.ifrn.edu.br/ojs/index.php/HOLOS/article/view/16340 10.15628/holos.2023.16340 |
url |
http://www2.ifrn.edu.br/ojs/index.php/HOLOS/article/view/16340 |
identifier_str_mv |
10.15628/holos.2023.16340 |
dc.language.iso.fl_str_mv |
por eng |
language |
por eng |
dc.relation.none.fl_str_mv |
http://www2.ifrn.edu.br/ojs/index.php/HOLOS/article/view/16340/3846 http://www2.ifrn.edu.br/ojs/index.php/HOLOS/article/view/16340/3847 |
dc.rights.driver.fl_str_mv |
https://creativecommons.org/licenses/by-nc-nd/4.0 info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
https://creativecommons.org/licenses/by-nc-nd/4.0 |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf application/pdf |
dc.coverage.none.fl_str_mv |
Barra Mansa, 1940 - 2023 Barra Mansa, 1940 - 2023 |
dc.publisher.none.fl_str_mv |
Instituto Federal de Educação, Ciência e Tecnologia do Rio Grande do Norte |
publisher.none.fl_str_mv |
Instituto Federal de Educação, Ciência e Tecnologia do Rio Grande do Norte |
dc.source.none.fl_str_mv |
HOLOS; v. 5 n. 39 (2023): v.5 (2023) 1807-1600 reponame:Holos instname:Instituto Federal do Rio Grande do Norte (IFRN) instacron:IFRN |
instname_str |
Instituto Federal do Rio Grande do Norte (IFRN) |
instacron_str |
IFRN |
institution |
IFRN |
reponame_str |
Holos |
collection |
Holos |
repository.name.fl_str_mv |
Holos - Instituto Federal do Rio Grande do Norte (IFRN) |
repository.mail.fl_str_mv |
holos@ifrn.edu.br||jyp.leite@ifrn.edu.br||propi@ifrn.edu.br |
_version_ |
1798951616764706816 |