A data pipeline for forest fire prediction in Pinamar

Autores
Martinez Saucedo, Ana; Inchausti, Pablo Ezequiel
Año de publicación
2023
Idioma
español castellano
Tipo de recurso
artículo
Estado
versión publicada
Descripción
In recent years, the severity of forest fires has reached worrying levels both internationally and nationally. However, thanks to the advance of technology, it is possible to predict forest fires occurrence and magnitude through Machine Learning models specially developed for this purpose. To achieve this goal, this paper describes the development of an automated data pipeline in the Python programming language that generates a forest. fires dataset specific to Pinamar area, thus allowing the subsequent training of predictive fire models. It is also configurable to gather meteorological, topographical and fuel data from other geographical areas.
Sociedad Argentina de Informática e Investigación Operativa
Materia
Ciencias Informáticas
incendios forestales
medio ambiente
datos abiertos
machine learning
remote sensing
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc/4.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/157006
Acceder
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oai_identifier_str oai:sedici.unlp.edu.ar:10915/157006
network_acronym_str SEDICI
repository_id_str 1329
network_name_str SEDICI (UNLP)
spelling A data pipeline for forest fire prediction in PinamarMartinez Saucedo, AnaInchausti, Pablo EzequielCiencias Informáticasincendios forestalesmedio ambientedatos abiertosmachine learningremote sensingIn recent years, the severity of forest fires has reached worrying levels both internationally and nationally. However, thanks to the advance of technology, it is possible to predict forest fires occurrence and magnitude through Machine Learning models specially developed for this purpose. To achieve this goal, this paper describes the development of an automated data pipeline in the Python programming language that generates a forest. fires dataset specific to Pinamar area, thus allowing the subsequent training of predictive fire models. It is also configurable to gather meteorological, topographical and fuel data from other geographical areas.Sociedad Argentina de Informática e Investigación Operativa2023-05info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionArticulohttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdf2-18http://sedici.unlp.edu.ar/handle/10915/157006spainfo:eu-repo/semantics/altIdentifier/url/https://publicaciones.sadio.org.ar/index.php/EJS/article/view/464info:eu-repo/semantics/altIdentifier/issn/1514-6774info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc/4.0/Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-10-15T11:32:42Zoai:sedici.unlp.edu.ar:10915/157006Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-10-15 11:32:42.242SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv A data pipeline for forest fire prediction in Pinamar
title A data pipeline for forest fire prediction in Pinamar
spellingShingle A data pipeline for forest fire prediction in Pinamar
Martinez Saucedo, Ana
Ciencias Informáticas
incendios forestales
medio ambiente
datos abiertos
machine learning
remote sensing
title_short A data pipeline for forest fire prediction in Pinamar
title_full A data pipeline for forest fire prediction in Pinamar
title_fullStr A data pipeline for forest fire prediction in Pinamar
title_full_unstemmed A data pipeline for forest fire prediction in Pinamar
title_sort A data pipeline for forest fire prediction in Pinamar
dc.creator.none.fl_str_mv Martinez Saucedo, Ana
Inchausti, Pablo Ezequiel
author Martinez Saucedo, Ana
author_facet Martinez Saucedo, Ana
Inchausti, Pablo Ezequiel
author_role author
author2 Inchausti, Pablo Ezequiel
author2_role author
dc.subject.none.fl_str_mv Ciencias Informáticas
incendios forestales
medio ambiente
datos abiertos
machine learning
remote sensing
topic Ciencias Informáticas
incendios forestales
medio ambiente
datos abiertos
machine learning
remote sensing
dc.description.none.fl_txt_mv In recent years, the severity of forest fires has reached worrying levels both internationally and nationally. However, thanks to the advance of technology, it is possible to predict forest fires occurrence and magnitude through Machine Learning models specially developed for this purpose. To achieve this goal, this paper describes the development of an automated data pipeline in the Python programming language that generates a forest. fires dataset specific to Pinamar area, thus allowing the subsequent training of predictive fire models. It is also configurable to gather meteorological, topographical and fuel data from other geographical areas.
Sociedad Argentina de Informática e Investigación Operativa
description In recent years, the severity of forest fires has reached worrying levels both internationally and nationally. However, thanks to the advance of technology, it is possible to predict forest fires occurrence and magnitude through Machine Learning models specially developed for this purpose. To achieve this goal, this paper describes the development of an automated data pipeline in the Python programming language that generates a forest. fires dataset specific to Pinamar area, thus allowing the subsequent training of predictive fire models. It is also configurable to gather meteorological, topographical and fuel data from other geographical areas.
publishDate 2023
dc.date.none.fl_str_mv 2023-05
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Articulo
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://sedici.unlp.edu.ar/handle/10915/157006
url http://sedici.unlp.edu.ar/handle/10915/157006
dc.language.none.fl_str_mv spa
language spa
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://publicaciones.sadio.org.ar/index.php/EJS/article/view/464
info:eu-repo/semantics/altIdentifier/issn/1514-6774
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
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Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc/4.0/
Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)
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dc.source.none.fl_str_mv reponame:SEDICI (UNLP)
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institution UNLP
repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
repository.mail.fl_str_mv alira@sedici.unlp.edu.ar
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