A data pipeline for forest fire prediction in Pinamar

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 achiev...

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Detalles Bibliográficos
Autores principales: Martinez Saucedo, Ana, Inchausti, Pablo Ezequiel
Formato: Articulo
Lenguaje:Español
Publicado: 2023
Materias:
Ciencias Informáticas
incendios forestales
medio ambiente
datos abiertos
machine learning
remote sensing
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/157006
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-157006
record_format dspace
spelling I19-R120-10915-1570062023-08-29T20:04:02Z http://sedici.unlp.edu.ar/handle/10915/157006 A data pipeline for forest fire prediction in Pinamar Martinez Saucedo, Ana Inchausti, Pablo Ezequiel 2023-05 2023-08-29T15:28:53Z es Ciencias Informáticas incendios forestales medio ambiente datos abiertos machine learning remote sensing 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 Articulo Articulo http://creativecommons.org/licenses/by-nc/4.0/ Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) application/pdf 2-18
institution Universidad Nacional de La Plata
institution_str I-19
repository_str R-120
collection SEDICI (UNLP)
language Español
topic Ciencias Informáticas
incendios forestales
medio ambiente
datos abiertos
machine learning
remote sensing
spellingShingle Ciencias Informáticas
incendios forestales
medio ambiente
datos abiertos
machine learning
remote sensing
Martinez Saucedo, Ana
Inchausti, Pablo Ezequiel
A data pipeline for forest fire prediction in Pinamar
topic_facet Ciencias Informáticas
incendios forestales
medio ambiente
datos abiertos
machine learning
remote sensing
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.
format Articulo
Articulo
author Martinez Saucedo, Ana
Inchausti, Pablo Ezequiel
author_facet Martinez Saucedo, Ana
Inchausti, Pablo Ezequiel
author_sort Martinez Saucedo, Ana
title A data pipeline for forest fire prediction in Pinamar
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 data pipeline for forest fire prediction in pinamar
publishDate 2023
url http://sedici.unlp.edu.ar/handle/10915/157006
work_keys_str_mv AT martinezsaucedoana adatapipelineforforestfirepredictioninpinamar
AT inchaustipabloezequiel adatapipelineforforestfirepredictioninpinamar
AT martinezsaucedoana datapipelineforforestfirepredictioninpinamar
AT inchaustipabloezequiel datapipelineforforestfirepredictioninpinamar
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