Lógica Fuzzy Aplicada Para Análise de Riscos de Incêndios Florestais Para o Bioma Amazônia, Brasil
Autor(a) principal: | |
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Data de Publicação: | 2024 |
Tipo de documento: | Tese |
Idioma: | por |
Título da fonte: | Repositório Institucional da Universidade Federal do Espírito Santo (riUfes) |
Texto Completo: | http://repositorio.ufes.br/handle/10/17381 |
Resumo: | Forest fires have emerged as an environmental issue of relevance in recent years, demanding the attention of researchers. Forest ecosystems, such as the Amazon biome, are increasingly threatened by forest fires. In this context, the main objective of this study was to develop a model capable of predicting the location and quantification of forest fires in the Amazon biome, Brazil, using Fuzzy logic. For the development of the fuzzy logic-based fire risk model, the following variables were used: temperature, water deficiency, precipitation, altitude, slope, aspect, population density, highways, and land use/cover. Three forest fire risk modeling approaches were conducted to test different membership functions for the same variable, referred to in this study as Forest Fire Risk 1 (FFR 1), employing the linear increasing, linear decreasing, generalized bell-shaped, and Gaussian fuzzy membership functions; in Forest Fire Risk 2 (FFR 2) modeling, large and small fuzzy membership functions were used; and in Forest Fire Risk 3 (FFR 3) modeling, the large fuzzy membership function was applied to all variables. Subsequently, a fire density map was generated using fire scar data from the MapBiomas database, followed by a Pearson correlation analysis to check the consistency of the maps and identify the most effective model. Among the three modeling approaches, FFR 1 showed the highest Pearson correlation value; thus, it was determined to be the best model capable of predicting the location of forest fires. The Amazon biome is classified as having a medium risk of fire occurrence, which corresponds to 30.05% of the entire study region, an area of approximately 1,263,561.00 km². Therefore, it is understood that the forest fire risk modeling was effective in predicting and quantifying the risk of forest fires for the Brazilian Amazon biome. |
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117Santos, Alexandre Rosa dos https://orcid.org/0000-0003-2617-9451http://lattes.cnpq.br/7125826645310758Carvalho, Rita de Cássia Freirehttps://orcid.org/0000-0003-1912-2430http://lattes.cnpq.br/6449713515797354Peluzio, Telma Machado de Oliveirahttps://orcid.org/0000-0003-0462-9239http://lattes.cnpq.br/2216111713065095Ferrari, Jéferson Luizhttps://orcid.org/0000-0001-5663-6428http://lattes.cnpq.br/5213847780149836Fiedler, Nilton Cesarhttps://orcid.org/0000-0002-4376-3660http://lattes.cnpq.br/8699171075880935Dias, Henrique Machadohttps://orcid.org/0000-0003-2217-7846http://lattes.cnpq.br/55658525088730922024-06-19T09:53:03Z2024-06-19T09:53:03Z2024-02-22Forest fires have emerged as an environmental issue of relevance in recent years, demanding the attention of researchers. Forest ecosystems, such as the Amazon biome, are increasingly threatened by forest fires. In this context, the main objective of this study was to develop a model capable of predicting the location and quantification of forest fires in the Amazon biome, Brazil, using Fuzzy logic. For the development of the fuzzy logic-based fire risk model, the following variables were used: temperature, water deficiency, precipitation, altitude, slope, aspect, population density, highways, and land use/cover. Three forest fire risk modeling approaches were conducted to test different membership functions for the same variable, referred to in this study as Forest Fire Risk 1 (FFR 1), employing the linear increasing, linear decreasing, generalized bell-shaped, and Gaussian fuzzy membership functions; in Forest Fire Risk 2 (FFR 2) modeling, large and small fuzzy membership functions were used; and in Forest Fire Risk 3 (FFR 3) modeling, the large fuzzy membership function was applied to all variables. Subsequently, a fire density map was generated using fire scar data from the MapBiomas database, followed by a Pearson correlation analysis to check the consistency of the maps and identify the most effective model. Among the three modeling approaches, FFR 1 showed the highest Pearson correlation value; thus, it was determined to be the best model capable of predicting the location of forest fires. The Amazon biome is classified as having a medium risk of fire occurrence, which corresponds to 30.05% of the entire study region, an area of approximately 1,263,561.00 km². Therefore, it is understood that the forest fire risk modeling was effective in predicting and quantifying the risk of forest fires for the Brazilian Amazon biome.Os incêndios florestais emergiram como uma problemática ambiental de relevância nos últimos anos, o que demanda atenção dos pesquisadores. Ecossistemas florestais, como o bioma Amazônia, estão, cada vez mais, ameaçados por incêndios florestais. Nesse contexto, o principal objetivo desse estudo foi desenvolver um modelo capaz de prever a localização e a quantificação de incêndios florestais, por meio da lógica Fuzzy para o bioma Amazônia, Brasil. Para a elaboração do modelo de risco de incêndio baseado na lógica Fuzzy, foram utilizadas as variáveis temperatura, deficiência hídrica, precipitação, altitude, declividade, aspectos das vertentes, densidade demográfica, rodovias e uso e cobertura da terra. Foram realizadas três modelagens de risco de incêndio florestal com o propósito de testar diferentes funções de pertinência para uma mesma variável, denominado, neste estudo, como Risco de Incêndio Florestal 1 (RIF 1), em que utilizou-se as funções de pertinência Fuzzy linear crescente, Fuzzy linear decrescente, Fuzzy Generalized Bell e Fuzzy Gaussian; na modelagem Risco de Incêndio Florestal 2 (RIF 2), utilizou-se as funções de pertinência Fuzzy Large e Fuzzy Small e para a modelagem Risco de Incêndio Florestal 3 (RIF 3), foi utilizada a função Fuzzy Large em todas as variáveis. Posteriormente, um mapa de densidade de incêndios foi gerado usando dados de cicatrizes de fogo do banco de dados MapBiomas, seguido por uma análise de correlação de Pearson para verificar a consistência dos mapas e identificar o modelo mais eficaz. Dentre as três modelagens, o RIF 1 apresentou o maior valor de correlação de Pearson, assim, esse foi constatado como o melhor modelo capaz de prever a localização de incêndios florestais. O bioma Amazônia é classificado como médio risco a ocorrência de incêndios, o que corresponde a 30,05 % de toda região de estudo, uma área de aproximadamente 1.263.561,00 km². Portanto, entende-se que a modelagem de risco de incêndios florestais foi eficaz para prever e quantificar o risco de incêndios florestais para o bioma Amazônia brasileiro.FAPESTexthttp://repositorio.ufes.br/handle/10/17381porUniversidade Federal do Espírito SantoDoutorado em Ciências FlorestaisPrograma de Pós-Graduação em Ciências FlorestaisUFESBRCentro de Ciências Agrárias e Engenhariassubject.br-rjbnÁrea(s) do conhecimento do documento (Tabela CNPq)Modelagem de riscobioma Amazôniageotecnologiaslógica FuzzyLógica Fuzzy Aplicada Para Análise de Riscos de Incêndios Florestais Para o Bioma Amazônia, Brasiltitle.alternativeinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/doctoralThesisinfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da Universidade Federal do Espírito Santo (riUfes)instname:Universidade Federal do Espírito Santo (UFES)instacron:UFESemail@ufes.brORIGINALRitadeCassiaFreireCarvalho-2024-tese.pdfRitadeCassiaFreireCarvalho-2024-tese.pdfapplication/pdf6373263http://repositorio.ufes.br/bitstreams/601c876d-308d-46b7-9129-06f3bc61957c/download185cb505688b5eb0603d63f199adc869MD51LICENSElicense.txtlicense.txttext/plain; charset=utf-81748http://repositorio.ufes.br/bitstreams/d31e0322-607c-4564-96c4-0686a789047e/download8a4605be74aa9ea9d79846c1fba20a33MD5210/173812024-08-29 11:25:09.042oai:repositorio.ufes.br:10/17381http://repositorio.ufes.brRepositório InstitucionalPUBhttp://repositorio.ufes.br/oai/requestopendoar:21082024-10-15T18:00:54.507021Repositório Institucional da Universidade Federal do Espírito Santo (riUfes) - Universidade Federal do Espírito Santo (UFES)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 |
dc.title.none.fl_str_mv |
Lógica Fuzzy Aplicada Para Análise de Riscos de Incêndios Florestais Para o Bioma Amazônia, Brasil |
dc.title.alternative.none.fl_str_mv |
title.alternative |
title |
Lógica Fuzzy Aplicada Para Análise de Riscos de Incêndios Florestais Para o Bioma Amazônia, Brasil |
spellingShingle |
Lógica Fuzzy Aplicada Para Análise de Riscos de Incêndios Florestais Para o Bioma Amazônia, Brasil Carvalho, Rita de Cássia Freire Área(s) do conhecimento do documento (Tabela CNPq) Modelagem de risco bioma Amazônia geotecnologias lógica Fuzzy subject.br-rjbn |
title_short |
Lógica Fuzzy Aplicada Para Análise de Riscos de Incêndios Florestais Para o Bioma Amazônia, Brasil |
title_full |
Lógica Fuzzy Aplicada Para Análise de Riscos de Incêndios Florestais Para o Bioma Amazônia, Brasil |
title_fullStr |
Lógica Fuzzy Aplicada Para Análise de Riscos de Incêndios Florestais Para o Bioma Amazônia, Brasil |
title_full_unstemmed |
Lógica Fuzzy Aplicada Para Análise de Riscos de Incêndios Florestais Para o Bioma Amazônia, Brasil |
title_sort |
Lógica Fuzzy Aplicada Para Análise de Riscos de Incêndios Florestais Para o Bioma Amazônia, Brasil |
author |
Carvalho, Rita de Cássia Freire |
author_facet |
Carvalho, Rita de Cássia Freire |
author_role |
author |
dc.contributor.authorID.none.fl_str_mv |
https://orcid.org/0000-0003-1912-2430 |
dc.contributor.authorLattes.none.fl_str_mv |
http://lattes.cnpq.br/6449713515797354 |
dc.contributor.advisor1.fl_str_mv |
Santos, Alexandre Rosa dos |
dc.contributor.advisor1ID.fl_str_mv |
https://orcid.org/0000-0003-2617-9451 |
dc.contributor.advisor1Lattes.fl_str_mv |
http://lattes.cnpq.br/7125826645310758 |
dc.contributor.author.fl_str_mv |
Carvalho, Rita de Cássia Freire |
dc.contributor.referee1.fl_str_mv |
Peluzio, Telma Machado de Oliveira |
dc.contributor.referee1ID.fl_str_mv |
https://orcid.org/0000-0003-0462-9239 |
dc.contributor.referee1Lattes.fl_str_mv |
http://lattes.cnpq.br/2216111713065095 |
dc.contributor.referee2.fl_str_mv |
Ferrari, Jéferson Luiz |
dc.contributor.referee2ID.fl_str_mv |
https://orcid.org/0000-0001-5663-6428 |
dc.contributor.referee2Lattes.fl_str_mv |
http://lattes.cnpq.br/5213847780149836 |
dc.contributor.referee3.fl_str_mv |
Fiedler, Nilton Cesar |
dc.contributor.referee3ID.fl_str_mv |
https://orcid.org/0000-0002-4376-3660 |
dc.contributor.referee3Lattes.fl_str_mv |
http://lattes.cnpq.br/8699171075880935 |
dc.contributor.referee4.fl_str_mv |
Dias, Henrique Machado |
dc.contributor.referee4ID.fl_str_mv |
https://orcid.org/0000-0003-2217-7846 |
dc.contributor.referee4Lattes.fl_str_mv |
http://lattes.cnpq.br/5565852508873092 |
contributor_str_mv |
Santos, Alexandre Rosa dos Peluzio, Telma Machado de Oliveira Ferrari, Jéferson Luiz Fiedler, Nilton Cesar Dias, Henrique Machado |
dc.subject.cnpq.fl_str_mv |
Área(s) do conhecimento do documento (Tabela CNPq) |
topic |
Área(s) do conhecimento do documento (Tabela CNPq) Modelagem de risco bioma Amazônia geotecnologias lógica Fuzzy subject.br-rjbn |
dc.subject.por.fl_str_mv |
Modelagem de risco bioma Amazônia geotecnologias lógica Fuzzy |
dc.subject.br-rjbn.none.fl_str_mv |
subject.br-rjbn |
description |
Forest fires have emerged as an environmental issue of relevance in recent years, demanding the attention of researchers. Forest ecosystems, such as the Amazon biome, are increasingly threatened by forest fires. In this context, the main objective of this study was to develop a model capable of predicting the location and quantification of forest fires in the Amazon biome, Brazil, using Fuzzy logic. For the development of the fuzzy logic-based fire risk model, the following variables were used: temperature, water deficiency, precipitation, altitude, slope, aspect, population density, highways, and land use/cover. Three forest fire risk modeling approaches were conducted to test different membership functions for the same variable, referred to in this study as Forest Fire Risk 1 (FFR 1), employing the linear increasing, linear decreasing, generalized bell-shaped, and Gaussian fuzzy membership functions; in Forest Fire Risk 2 (FFR 2) modeling, large and small fuzzy membership functions were used; and in Forest Fire Risk 3 (FFR 3) modeling, the large fuzzy membership function was applied to all variables. Subsequently, a fire density map was generated using fire scar data from the MapBiomas database, followed by a Pearson correlation analysis to check the consistency of the maps and identify the most effective model. Among the three modeling approaches, FFR 1 showed the highest Pearson correlation value; thus, it was determined to be the best model capable of predicting the location of forest fires. The Amazon biome is classified as having a medium risk of fire occurrence, which corresponds to 30.05% of the entire study region, an area of approximately 1,263,561.00 km². Therefore, it is understood that the forest fire risk modeling was effective in predicting and quantifying the risk of forest fires for the Brazilian Amazon biome. |
publishDate |
2024 |
dc.date.accessioned.fl_str_mv |
2024-06-19T09:53:03Z |
dc.date.available.fl_str_mv |
2024-06-19T09:53:03Z |
dc.date.issued.fl_str_mv |
2024-02-22 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
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info:eu-repo/semantics/doctoralThesis |
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doctoralThesis |
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publishedVersion |
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http://repositorio.ufes.br/handle/10/17381 |
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http://repositorio.ufes.br/handle/10/17381 |
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por |
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por |
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info:eu-repo/semantics/openAccess |
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openAccess |
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Text |
dc.publisher.none.fl_str_mv |
Universidade Federal do Espírito Santo Doutorado em Ciências Florestais |
dc.publisher.program.fl_str_mv |
Programa de Pós-Graduação em Ciências Florestais |
dc.publisher.initials.fl_str_mv |
UFES |
dc.publisher.country.fl_str_mv |
BR |
dc.publisher.department.fl_str_mv |
Centro de Ciências Agrárias e Engenharias |
publisher.none.fl_str_mv |
Universidade Federal do Espírito Santo Doutorado em Ciências Florestais |
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