Machine Learning Approach for Predicting Seasonal Risk of Forest Fires in Morocco
Autor(a) principal: | |
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Data de Publicação: | 2019 |
Outros Autores: | , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Biodiversidade Brasileira |
Texto Completo: | https://revistaeletronica.icmbio.gov.br/BioBR/article/view/1116 |
Resumo: | Predicting forest fire risks constitutes a significant component of forest fire management and combatting strategies. It plays a major role in resource allocation, mitigation and recovery efforts. The purpose of this study is to develop a predictive model of seasonal forest fire risk using machine learning approach. Used data consists on 2130 forest fire events that occurred between 1997 and 2011 and their locations, the biophysical characteristics of such locations are represented by 39 variables derived from a digital elevation model, meteorological variables (including precipitation, wind, evaportranspiration, …), and vegetation characteristics derived from 288 satellite images (using the normalized difference vegetation index of MODIS and Landsat images). Random forest algorithm was used to link between theses predictors and seasonal forest fire risk. The trained model performed a good predictive ability (83% of the accuracy, p-value=0.013). It showed that only hevernal precipitations have a strong influence on fire occurrence and seasonal severity in the fire season. Such fact could be explained by the contribution of the rainfall to primary build-up and for fuel dryness. Then, according to the developed model, it become easier to predict seasonal risk knowing the hivernal precipitation and then to best plan for rational uses of fires combatting means. |
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Machine Learning Approach for Predicting Seasonal Risk of Forest Fires in Morocco Machine learning approach for predicting seasonal risk of forest fires in Morocco Machine Learning Approach for Predicting Seasonal Risk of Forest Fires in Morocco Forest firemachine learning predictive modelrandom forestseasonal riskPredicting forest fire risks constitutes a significant component of forest fire management and combatting strategies. It plays a major role in resource allocation, mitigation and recovery efforts. The purpose of this study is to develop a predictive model of seasonal forest fire risk using machine learning approach. Used data consists on 2130 forest fire events that occurred between 1997 and 2011 and their locations, the biophysical characteristics of such locations are represented by 39 variables derived from a digital elevation model, meteorological variables (including precipitation, wind, evaportranspiration, …), and vegetation characteristics derived from 288 satellite images (using the normalized difference vegetation index of MODIS and Landsat images). Random forest algorithm was used to link between theses predictors and seasonal forest fire risk. The trained model performed a good predictive ability (83% of the accuracy, p-value=0.013). It showed that only hevernal precipitations have a strong influence on fire occurrence and seasonal severity in the fire season. Such fact could be explained by the contribution of the rainfall to primary build-up and for fuel dryness. Then, according to the developed model, it become easier to predict seasonal risk knowing the hivernal precipitation and then to best plan for rational uses of fires combatting means.Predicting forest fire risks constitutes a significant component of forest fire management and combatting strategies. It plays a major role in resource allocation, mitigation and recovery efforts. The purpose of this study is to develop a predictive model of seasonal forest fire risk using machine learning approach. Used data consists on 2130 forest fire events that occurred between 1997 and 2011 and their locations, the biophysical characteristics of such locations are represented by 39 variables derived from a digital elevation model, meteorological variables (including precipitation, wind, evaportranspiration, …), and vegetation characteristics derived from 288 satellite images (using the normalized difference vegetation index of MODIS and Landsat images). Random forest algorithm was used to link between theses predictors and seasonal forest fire risk. The trained model performed a good predictive ability (83% of the accuracy, p-value=0.013). It showed that only hevernal precipitations have a strong influence on fire occurrence and seasonal severity in the fire season. Such fact could be explained by the contribution of the rainfall to primary build-up and for fuel dryness. Then, according to the developed model, it become easier to predict seasonal risk knowing the hivernal precipitation and then to best plan for rational uses of fires combatting means.Predicting forest fire risks constitutes a significant component of forest fire management and combatting strategies. It plays a major role in resource allocation, mitigation and recovery efforts. The purpose of this study is to develop a predictive model of seasonal forest fire risk using machine learning approach. Used data consists on 2130 forest fire events that occurred between 1997 and 2011 and their locations, the biophysical characteristics of such locations are represented by 39 variables derived from a digital elevation model, meteorological variables (including precipitation, wind, evaportranspiration, …), and vegetation characteristics derived from 288 satellite images (using the normalized difference vegetation index of MODIS and Landsat images). Random forest algorithm was used to link between theses predictors and seasonal forest fire risk. The trained model performed a good predictive ability (83% of the accuracy, p-value=0.013). It showed that only hevernal precipitations have a strong influence on fire occurrence and seasonal severity in the fire season. Such fact could be explained by the contribution of the rainfall to primary build-up and for fuel dryness. Then, according to the developed model, it become easier to predict seasonal risk knowing the hivernal precipitation and then to best plan for rational uses of fires combatting means.Instituto Chico Mendes de Conservação da Biodiversidade (ICMBio)2019-11-15info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://revistaeletronica.icmbio.gov.br/BioBR/article/view/111610.37002/biodiversidadebrasileira.v9i1.1116Biodiversidade Brasileira ; v. 9 n. 1 (2019): Wildfire Conference: Resumos; 187Biodiversidade Brasileira ; Vol. 9 No. 1 (2019): Wildfire Conference: Resumos; 187Biodiversidade Brasileira ; Vol. 9 Núm. 1 (2019): Wildfire Conference: Resumos; 1872236-288610.37002/biodiversidadebrasileira.v9i1reponame:Biodiversidade Brasileirainstname:Instituto Chico Mendes de Conservação da Biodiversidade (ICMBIO)instacron:ICMBIOenghttps://revistaeletronica.icmbio.gov.br/BioBR/article/view/1116/835Copyright (c) 2021 Biodiversidade Brasileira - BioBrasilhttps://creativecommons.org/licenses/by-nc-nd/4.0info:eu-repo/semantics/openAccessMharzi Alaoui, HichamAssali, FouadHajji, HichamLahssini, SaidAadel, TaoufikMoukrim, Said2023-05-09T12:56:02Zoai:revistaeletronica.icmbio.gov.br:article/1116Revistahttps://revistaeletronica.icmbio.gov.br/BioBRPUBhttps://revistaeletronica.icmbio.gov.br/BioBR/oaifernanda.oliveto@icmbio.gov.br || katia.ribeiro@icmbio.gov.br2236-28862236-2886opendoar:2023-05-09T12:56:02Biodiversidade Brasileira - Instituto Chico Mendes de Conservação da Biodiversidade (ICMBIO)false |
dc.title.none.fl_str_mv |
Machine Learning Approach for Predicting Seasonal Risk of Forest Fires in Morocco Machine learning approach for predicting seasonal risk of forest fires in Morocco Machine Learning Approach for Predicting Seasonal Risk of Forest Fires in Morocco |
title |
Machine Learning Approach for Predicting Seasonal Risk of Forest Fires in Morocco |
spellingShingle |
Machine Learning Approach for Predicting Seasonal Risk of Forest Fires in Morocco Mharzi Alaoui, Hicham Forest fire machine learning predictive model random forest seasonal risk |
title_short |
Machine Learning Approach for Predicting Seasonal Risk of Forest Fires in Morocco |
title_full |
Machine Learning Approach for Predicting Seasonal Risk of Forest Fires in Morocco |
title_fullStr |
Machine Learning Approach for Predicting Seasonal Risk of Forest Fires in Morocco |
title_full_unstemmed |
Machine Learning Approach for Predicting Seasonal Risk of Forest Fires in Morocco |
title_sort |
Machine Learning Approach for Predicting Seasonal Risk of Forest Fires in Morocco |
author |
Mharzi Alaoui, Hicham |
author_facet |
Mharzi Alaoui, Hicham Assali, Fouad Hajji, Hicham Lahssini, Said Aadel, Taoufik Moukrim, Said |
author_role |
author |
author2 |
Assali, Fouad Hajji, Hicham Lahssini, Said Aadel, Taoufik Moukrim, Said |
author2_role |
author author author author author |
dc.contributor.author.fl_str_mv |
Mharzi Alaoui, Hicham Assali, Fouad Hajji, Hicham Lahssini, Said Aadel, Taoufik Moukrim, Said |
dc.subject.por.fl_str_mv |
Forest fire machine learning predictive model random forest seasonal risk |
topic |
Forest fire machine learning predictive model random forest seasonal risk |
description |
Predicting forest fire risks constitutes a significant component of forest fire management and combatting strategies. It plays a major role in resource allocation, mitigation and recovery efforts. The purpose of this study is to develop a predictive model of seasonal forest fire risk using machine learning approach. Used data consists on 2130 forest fire events that occurred between 1997 and 2011 and their locations, the biophysical characteristics of such locations are represented by 39 variables derived from a digital elevation model, meteorological variables (including precipitation, wind, evaportranspiration, …), and vegetation characteristics derived from 288 satellite images (using the normalized difference vegetation index of MODIS and Landsat images). Random forest algorithm was used to link between theses predictors and seasonal forest fire risk. The trained model performed a good predictive ability (83% of the accuracy, p-value=0.013). It showed that only hevernal precipitations have a strong influence on fire occurrence and seasonal severity in the fire season. Such fact could be explained by the contribution of the rainfall to primary build-up and for fuel dryness. Then, according to the developed model, it become easier to predict seasonal risk knowing the hivernal precipitation and then to best plan for rational uses of fires combatting means. |
publishDate |
2019 |
dc.date.none.fl_str_mv |
2019-11-15 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://revistaeletronica.icmbio.gov.br/BioBR/article/view/1116 10.37002/biodiversidadebrasileira.v9i1.1116 |
url |
https://revistaeletronica.icmbio.gov.br/BioBR/article/view/1116 |
identifier_str_mv |
10.37002/biodiversidadebrasileira.v9i1.1116 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
https://revistaeletronica.icmbio.gov.br/BioBR/article/view/1116/835 |
dc.rights.driver.fl_str_mv |
Copyright (c) 2021 Biodiversidade Brasileira - BioBrasil https://creativecommons.org/licenses/by-nc-nd/4.0 info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Copyright (c) 2021 Biodiversidade Brasileira - BioBrasil https://creativecommons.org/licenses/by-nc-nd/4.0 |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Instituto Chico Mendes de Conservação da Biodiversidade (ICMBio) |
publisher.none.fl_str_mv |
Instituto Chico Mendes de Conservação da Biodiversidade (ICMBio) |
dc.source.none.fl_str_mv |
Biodiversidade Brasileira ; v. 9 n. 1 (2019): Wildfire Conference: Resumos; 187 Biodiversidade Brasileira ; Vol. 9 No. 1 (2019): Wildfire Conference: Resumos; 187 Biodiversidade Brasileira ; Vol. 9 Núm. 1 (2019): Wildfire Conference: Resumos; 187 2236-2886 10.37002/biodiversidadebrasileira.v9i1 reponame:Biodiversidade Brasileira instname:Instituto Chico Mendes de Conservação da Biodiversidade (ICMBIO) instacron:ICMBIO |
instname_str |
Instituto Chico Mendes de Conservação da Biodiversidade (ICMBIO) |
instacron_str |
ICMBIO |
institution |
ICMBIO |
reponame_str |
Biodiversidade Brasileira |
collection |
Biodiversidade Brasileira |
repository.name.fl_str_mv |
Biodiversidade Brasileira - Instituto Chico Mendes de Conservação da Biodiversidade (ICMBIO) |
repository.mail.fl_str_mv |
fernanda.oliveto@icmbio.gov.br || katia.ribeiro@icmbio.gov.br |
_version_ |
1797042391724589056 |