Computational science for forest fire prediction

Autores
Bianchini, Germán; Caymes Scutari, Paola
Año de publicación
2024
Idioma
español castellano
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
Forest fires are a very serious hazard that, every year, causes significant damage around the world from the ecological, social, economical and human point of view. These hazards are particularly dangerous when meteorological conditions are extreme with dry and hot seasons or strong wind. The fire fighting should have at its disposal the most advanced resources and tools to help the use of available resources in the most efficient way to diminish fire effects as much as possible. The problem of forest fire spread prediction presents a high degree of complexity due in large part to the limitations for providing accurate input parameters in real time (e.g., wind speed, temperature, moisture of the soil, etc.). The inaccuracies present in the measurements, the models, and the computational implementation constitute different sources of uncertainty. This uncertainty has led to the development of computational methods that seek to obtain better predictions. In this article, we present a line of research for the development of uncertainty reduction methods for the prediction of propagation phenomena (so called DDM-MOS in the taxonomy). Each method in this family combines the strength of different elements: evolutionary computing, statistics, parallel computing, and novelty search, to make decisions according to the result and trend of a set of simulations. Due to the characteristics of the methods, this approach is also feasible to be applied for the prediction of other types of propagation phenomena such as floods, avalanches, etc.
Fil: Universidad Tecnológica Nacional. Faculatd Regional Mendoza, Argentina
Materia
Decision support systems, Wildfire propagation prediction, Uncertainty reduction, Novelty search
Nivel de accesibilidad
acceso abierto
Condiciones de uso
CC0 1.0 Universal
Repositorio
Repositorio Institucional Abierto (UTN)
Institución
Universidad Tecnológica Nacional
OAI Identificador
oai:ria.utn.edu.ar:20.500.12272/12916
Acceder
id RIAUTN_a34e908bb77f598c3d2d89428000bdef
oai_identifier_str oai:ria.utn.edu.ar:20.500.12272/12916
network_acronym_str RIAUTN
repository_id_str a
network_name_str Repositorio Institucional Abierto (UTN)
spelling Computational science for forest fire predictionBianchini, GermánCaymes Scutari, PaolaDecision support systems, Wildfire propagation prediction, Uncertainty reduction, Novelty searchForest fires are a very serious hazard that, every year, causes significant damage around the world from the ecological, social, economical and human point of view. These hazards are particularly dangerous when meteorological conditions are extreme with dry and hot seasons or strong wind. The fire fighting should have at its disposal the most advanced resources and tools to help the use of available resources in the most efficient way to diminish fire effects as much as possible. The problem of forest fire spread prediction presents a high degree of complexity due in large part to the limitations for providing accurate input parameters in real time (e.g., wind speed, temperature, moisture of the soil, etc.). The inaccuracies present in the measurements, the models, and the computational implementation constitute different sources of uncertainty. This uncertainty has led to the development of computational methods that seek to obtain better predictions. In this article, we present a line of research for the development of uncertainty reduction methods for the prediction of propagation phenomena (so called DDM-MOS in the taxonomy). Each method in this family combines the strength of different elements: evolutionary computing, statistics, parallel computing, and novelty search, to make decisions according to the result and trend of a set of simulations. Due to the characteristics of the methods, this approach is also feasible to be applied for the prediction of other types of propagation phenomena such as floods, avalanches, etc.Fil: Universidad Tecnológica Nacional. Faculatd Regional Mendoza, ArgentinaUniversidad Tecnológica Nacional. Faculatd Regional Mendoza2025-05-12T12:36:10Z2024-10-01info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciapdfapplication/pdfWorkshop GEM24-Geomatics in Environmental Monitoringhttps://hdl.handle.net/20.500.12272/12916spaPID TETEUME0008760TCinfo:eu-repo/semantics/openAccessCC0 1.0 Universalhttp://creativecommons.org/publicdomain/zero/1.0/Universidad Tecnológica Nacional, Facultad Regional MendozaCC BY-NC-SA (Autoría – No Comercial – Compartir igual)reponame:Repositorio Institucional Abierto (UTN)instname:Universidad Tecnológica Nacional2026-09-24T12:44:07Zoai:ria.utn.edu.ar:20.500.12272/12916instacron:UTNInstitucionalhttp://ria.utn.edu.ar/Universidad públicaNo correspondehttp://ria.utn.edu.ar/oaigestionria@rec.utn.edu.ar; fsuarez@rec.utn.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:a2026-09-24 12:44:08.268Repositorio Institucional Abierto (UTN) - Universidad Tecnológica Nacionalfalse
dc.title.none.fl_str_mv Computational science for forest fire prediction
title Computational science for forest fire prediction
spellingShingle Computational science for forest fire prediction
Bianchini, Germán
Decision support systems, Wildfire propagation prediction, Uncertainty reduction, Novelty search
title_short Computational science for forest fire prediction
title_full Computational science for forest fire prediction
title_fullStr Computational science for forest fire prediction
title_full_unstemmed Computational science for forest fire prediction
title_sort Computational science for forest fire prediction
dc.creator.none.fl_str_mv Bianchini, Germán
Caymes Scutari, Paola
author Bianchini, Germán
author_facet Bianchini, Germán
Caymes Scutari, Paola
author_role author
author2 Caymes Scutari, Paola
author2_role author
dc.subject.none.fl_str_mv Decision support systems, Wildfire propagation prediction, Uncertainty reduction, Novelty search
topic Decision support systems, Wildfire propagation prediction, Uncertainty reduction, Novelty search
dc.description.none.fl_txt_mv Forest fires are a very serious hazard that, every year, causes significant damage around the world from the ecological, social, economical and human point of view. These hazards are particularly dangerous when meteorological conditions are extreme with dry and hot seasons or strong wind. The fire fighting should have at its disposal the most advanced resources and tools to help the use of available resources in the most efficient way to diminish fire effects as much as possible. The problem of forest fire spread prediction presents a high degree of complexity due in large part to the limitations for providing accurate input parameters in real time (e.g., wind speed, temperature, moisture of the soil, etc.). The inaccuracies present in the measurements, the models, and the computational implementation constitute different sources of uncertainty. This uncertainty has led to the development of computational methods that seek to obtain better predictions. In this article, we present a line of research for the development of uncertainty reduction methods for the prediction of propagation phenomena (so called DDM-MOS in the taxonomy). Each method in this family combines the strength of different elements: evolutionary computing, statistics, parallel computing, and novelty search, to make decisions according to the result and trend of a set of simulations. Due to the characteristics of the methods, this approach is also feasible to be applied for the prediction of other types of propagation phenomena such as floods, avalanches, etc.
Fil: Universidad Tecnológica Nacional. Faculatd Regional Mendoza, Argentina
description Forest fires are a very serious hazard that, every year, causes significant damage around the world from the ecological, social, economical and human point of view. These hazards are particularly dangerous when meteorological conditions are extreme with dry and hot seasons or strong wind. The fire fighting should have at its disposal the most advanced resources and tools to help the use of available resources in the most efficient way to diminish fire effects as much as possible. The problem of forest fire spread prediction presents a high degree of complexity due in large part to the limitations for providing accurate input parameters in real time (e.g., wind speed, temperature, moisture of the soil, etc.). The inaccuracies present in the measurements, the models, and the computational implementation constitute different sources of uncertainty. This uncertainty has led to the development of computational methods that seek to obtain better predictions. In this article, we present a line of research for the development of uncertainty reduction methods for the prediction of propagation phenomena (so called DDM-MOS in the taxonomy). Each method in this family combines the strength of different elements: evolutionary computing, statistics, parallel computing, and novelty search, to make decisions according to the result and trend of a set of simulations. Due to the characteristics of the methods, this approach is also feasible to be applied for the prediction of other types of propagation phenomena such as floods, avalanches, etc.
publishDate 2024
dc.date.none.fl_str_mv 2024-10-01
2025-05-12T12:36:10Z
dc.type.none.fl_str_mv info:eu-repo/semantics/conferenceObject
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_5794
info:ar-repo/semantics/documentoDeConferencia
format conferenceObject
status_str publishedVersion
dc.identifier.none.fl_str_mv Workshop GEM24-Geomatics in Environmental Monitoring
https://hdl.handle.net/20.500.12272/12916
identifier_str_mv Workshop GEM24-Geomatics in Environmental Monitoring
url https://hdl.handle.net/20.500.12272/12916
dc.language.none.fl_str_mv spa
language spa
dc.relation.none.fl_str_mv PID TETEUME0008760TC
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
CC0 1.0 Universal
http://creativecommons.org/publicdomain/zero/1.0/
Universidad Tecnológica Nacional, Facultad Regional Mendoza
CC BY-NC-SA (Autoría – No Comercial – Compartir igual)
eu_rights_str_mv openAccess
rights_invalid_str_mv CC0 1.0 Universal
http://creativecommons.org/publicdomain/zero/1.0/
Universidad Tecnológica Nacional, Facultad Regional Mendoza
CC BY-NC-SA (Autoría – No Comercial – Compartir igual)
dc.format.none.fl_str_mv pdf
application/pdf
dc.publisher.none.fl_str_mv Universidad Tecnológica Nacional. Faculatd Regional Mendoza
publisher.none.fl_str_mv Universidad Tecnológica Nacional. Faculatd Regional Mendoza
dc.source.none.fl_str_mv reponame:Repositorio Institucional Abierto (UTN)
instname:Universidad Tecnológica Nacional
reponame_str Repositorio Institucional Abierto (UTN)
collection Repositorio Institucional Abierto (UTN)
instname_str Universidad Tecnológica Nacional
repository.name.fl_str_mv Repositorio Institucional Abierto (UTN) - Universidad Tecnológica Nacional
repository.mail.fl_str_mv gestionria@rec.utn.edu.ar; fsuarez@rec.utn.edu.ar
_version_ 1877230870893428736
score 13.24418

Publicaciones similares