Application of machine learning and deep learning for modeling a water basin
Autor(a) principal: | |
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Data de Publicação: | 2023 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | por |
Título da fonte: | P@ranoá |
Texto Completo: | https://periodicos.unb.br/index.php/paranoa/article/view/47488 |
Resumo: | Economic development associated with aspects such as social inequality is a process that leads to the deterioration of environmental resources. It is essential to improve technologies to combat the effects of natural disasters, such as enhancing the processes of rainfall-to-runoff transformation and modeling hydrological phenomena in river basins. The objective of this scientific communication was to obtain hydrological behavior predictions in the Bananal River Basin using machine learning techniques. The gathered information was subjected to the following models: deep learning artificial neural networks (InceptionTime, Resnet, FCN, and ResCNN) and machine learning models (linear regression, decision tree, and support-vector machine), as well as gradient boosting, to analyze consistent historical data and compare precipitation patterns and streamflow (rainfall-runoff model) in the watercourse. Daily average time series data from 2002 to 2014 were used for training to establish optimized behavior for 2015. It was concluded that the architecture with the highest performance, based on the coefficient of determination (R²), was InceptionTime, with an RMSE of 4.62 and MAE of 2.36. The decision tree model achieved the best overall performance with an R² of 0.73. |
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Application of machine learning and deep learning for modeling a water basinAplicación de aprendizaje máquino y aprendizaje profundo para modelar una cuenca de aguaAplicação de machine learning e deep learning para modelagem de uma bacia hidrográficaDeep Learning, inundaçõeschuva-vazãomodelagemInceptionTimeinundacionesagua de lluviaFlujo de diseñomodeladSimulación ComputacionalFloodrainfallDesign flowComputational modellingEconomic development associated with aspects such as social inequality is a process that leads to the deterioration of environmental resources. It is essential to improve technologies to combat the effects of natural disasters, such as enhancing the processes of rainfall-to-runoff transformation and modeling hydrological phenomena in river basins. The objective of this scientific communication was to obtain hydrological behavior predictions in the Bananal River Basin using machine learning techniques. The gathered information was subjected to the following models: deep learning artificial neural networks (InceptionTime, Resnet, FCN, and ResCNN) and machine learning models (linear regression, decision tree, and support-vector machine), as well as gradient boosting, to analyze consistent historical data and compare precipitation patterns and streamflow (rainfall-runoff model) in the watercourse. Daily average time series data from 2002 to 2014 were used for training to establish optimized behavior for 2015. It was concluded that the architecture with the highest performance, based on the coefficient of determination (R²), was InceptionTime, with an RMSE of 4.62 and MAE of 2.36. The decision tree model achieved the best overall performance with an R² of 0.73.El desarrollo económico asociado con aspectos como la desigualdad social es un proceso que provoca la deterioración de los recursos ambientales. Es urgente mejorar las tecnologías para combatir los efectos de los desastres naturales, como el mejoramiento de los procesos de lluvia en caudal y la modelización de los fenómenos hidrológicos en cuencas hidrográficas. El objetivo fue obtener, mediante técnicas de aprendizaje automático, pronósticos del comportamiento hidrológico en la Cuenca del Río Bananal. Esta información recopilada se sometió a los siguientes modelos: redes neuronales artificiales de aprendizaje profundo (Deep Learning - InceptionTime, Resnet, FCN y ResCNN) y modelos de aprendizaje automático (Machine Learning - regresión lineal, árbol de decisión y máquina de vectores de soporte), así como impulso de gradiente para generar series históricas consistentes y comparar los patrones de precipitación y flujo (modelo de lluvia-caudal) del curso del agua. Se utilizaron las series temporales promedio diarias de 2002 a 2014 para el entrenamiento, con el fin de establecer un comportamiento optimizado para 2015. Se pudo concluir que el mejor rendimiento, según el coeficiente R², fue InceptionTime, con un RMSE de 4,62 y un MAE de 2,36. El modelo de árbol de decisión obtuvo el mejor R² de 0,73.O desenvolvimento econômico associado a aspectos como a desigualdade social são processos que provocam a deterioração de recursos ambientais. São emergenciais o aperfeiçoamento das tecnologias para combater os efeitos dos desastres naturais, como a melhoria dos processos de transformação de chuva em vazão e da modelagem dos fenômenos hidrológicos em bacias hidrográficas. O objetivo desta comunicação científica foi obter, com o auxílio de técnicas de machine learning, previsões do comportamento hidrológico na Bacia do Rio Bananal. Essas informações reunidas foram submetidas aos seguintes modelos: redes neurais artificiais de aprendizado profundo (Deep Learning - InceptionTime, Resnet, FCN e ResCNN) e de aprendizado de máquina (Machine Learning - regressão linear, árvore de decisão e support-vector machine), e gradient boosting para levantar séries históricas consistentes e comparar os padrões de precipitação e da vazão (modelo chuva-vazão) do curso hídrico. Foram utilizadas as séries temporais médias diárias de 2002 a 2014 para treinamento a fim de estabelecer um comportamento otimizado para 2015. Foi possível concluir que o coeficiente R² com a melhor performance foi da arquitetura InceptionTime, com RMSE de 4.62, MAE de 2.36. O resultado com amior desempenho foi a árvore de decisão com R2 de 0.73.Programa de Pós-graduação em Arquitetura e Urbanismo, Faculdade de Arquitetura e Urbanismo2023-09-06info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionArtigo avaliado pelos paresapplication/pdfhttps://periodicos.unb.br/index.php/paranoa/article/view/4748810.18830/issn.1679-0944.n34.2023.20Paranoá; Vol. 16 No. 34 (2023): January/June Edition; 1-21Paranoá; v. 16 n. 34 (2023): Fluxo Contínuo - Janeiro/Junho; 1-211679-09441677-7395reponame:P@ranoáinstname:Universidade de Brasília (UnB)instacron:UNBporhttps://periodicos.unb.br/index.php/paranoa/article/view/47488/38484Copyright (c) 2023 Paranoáhttps://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccessde Azevedo Silva, Viníciusde Feiras Souza, RafaelPeixoto Oliveira, MateusLledo dos Santos, Francisco2024-02-08T15:00:47Zoai:ojs.pkp.sfu.ca:article/47488Revistahttps://periodicos.unb.br/index.php/paranoaPUBhttps://periodicos.unb.br/index.php/paranoa/oaiparanoa@unb.br1679-09441677-7395opendoar:2024-02-08T15:00:47P@ranoá - Universidade de Brasília (UnB)false |
dc.title.none.fl_str_mv |
Application of machine learning and deep learning for modeling a water basin Aplicación de aprendizaje máquino y aprendizaje profundo para modelar una cuenca de agua Aplicação de machine learning e deep learning para modelagem de uma bacia hidrográfica |
title |
Application of machine learning and deep learning for modeling a water basin |
spellingShingle |
Application of machine learning and deep learning for modeling a water basin de Azevedo Silva, Vinícius Deep Learning , inundações chuva-vazão modelagem InceptionTime inundaciones agua de lluvia Flujo de diseño modelad Simulación Computacional Flood rainfall Design flow Computational modelling |
title_short |
Application of machine learning and deep learning for modeling a water basin |
title_full |
Application of machine learning and deep learning for modeling a water basin |
title_fullStr |
Application of machine learning and deep learning for modeling a water basin |
title_full_unstemmed |
Application of machine learning and deep learning for modeling a water basin |
title_sort |
Application of machine learning and deep learning for modeling a water basin |
author |
de Azevedo Silva, Vinícius |
author_facet |
de Azevedo Silva, Vinícius de Feiras Souza, Rafael Peixoto Oliveira, Mateus Lledo dos Santos, Francisco |
author_role |
author |
author2 |
de Feiras Souza, Rafael Peixoto Oliveira, Mateus Lledo dos Santos, Francisco |
author2_role |
author author author |
dc.contributor.author.fl_str_mv |
de Azevedo Silva, Vinícius de Feiras Souza, Rafael Peixoto Oliveira, Mateus Lledo dos Santos, Francisco |
dc.subject.por.fl_str_mv |
Deep Learning , inundações chuva-vazão modelagem InceptionTime inundaciones agua de lluvia Flujo de diseño modelad Simulación Computacional Flood rainfall Design flow Computational modelling |
topic |
Deep Learning , inundações chuva-vazão modelagem InceptionTime inundaciones agua de lluvia Flujo de diseño modelad Simulación Computacional Flood rainfall Design flow Computational modelling |
description |
Economic development associated with aspects such as social inequality is a process that leads to the deterioration of environmental resources. It is essential to improve technologies to combat the effects of natural disasters, such as enhancing the processes of rainfall-to-runoff transformation and modeling hydrological phenomena in river basins. The objective of this scientific communication was to obtain hydrological behavior predictions in the Bananal River Basin using machine learning techniques. The gathered information was subjected to the following models: deep learning artificial neural networks (InceptionTime, Resnet, FCN, and ResCNN) and machine learning models (linear regression, decision tree, and support-vector machine), as well as gradient boosting, to analyze consistent historical data and compare precipitation patterns and streamflow (rainfall-runoff model) in the watercourse. Daily average time series data from 2002 to 2014 were used for training to establish optimized behavior for 2015. It was concluded that the architecture with the highest performance, based on the coefficient of determination (R²), was InceptionTime, with an RMSE of 4.62 and MAE of 2.36. The decision tree model achieved the best overall performance with an R² of 0.73. |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023-09-06 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Artigo avaliado pelos pares |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://periodicos.unb.br/index.php/paranoa/article/view/47488 10.18830/issn.1679-0944.n34.2023.20 |
url |
https://periodicos.unb.br/index.php/paranoa/article/view/47488 |
identifier_str_mv |
10.18830/issn.1679-0944.n34.2023.20 |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.relation.none.fl_str_mv |
https://periodicos.unb.br/index.php/paranoa/article/view/47488/38484 |
dc.rights.driver.fl_str_mv |
Copyright (c) 2023 Paranoá https://creativecommons.org/licenses/by/4.0 info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Copyright (c) 2023 Paranoá https://creativecommons.org/licenses/by/4.0 |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Programa de Pós-graduação em Arquitetura e Urbanismo, Faculdade de Arquitetura e Urbanismo |
publisher.none.fl_str_mv |
Programa de Pós-graduação em Arquitetura e Urbanismo, Faculdade de Arquitetura e Urbanismo |
dc.source.none.fl_str_mv |
Paranoá; Vol. 16 No. 34 (2023): January/June Edition; 1-21 Paranoá; v. 16 n. 34 (2023): Fluxo Contínuo - Janeiro/Junho; 1-21 1679-0944 1677-7395 reponame:P@ranoá instname:Universidade de Brasília (UnB) instacron:UNB |
instname_str |
Universidade de Brasília (UnB) |
instacron_str |
UNB |
institution |
UNB |
reponame_str |
P@ranoá |
collection |
P@ranoá |
repository.name.fl_str_mv |
P@ranoá - Universidade de Brasília (UnB) |
repository.mail.fl_str_mv |
paranoa@unb.br |
_version_ |
1797066980618928128 |