Prepositioning of resources for fire suppression problem under uncertainty
| Autor(a) principal: | |
|---|---|
| Data de Publicação: | 2024 |
| Tipo de documento: | Dissertação |
| Idioma: | eng |
| Título da fonte: | Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) |
| Texto Completo: | http://hdl.handle.net/10773/42762 |
Resumo: | Wildfires are natural events that, in recent decades, have been increasingly more frequent and severe, and when not contained, can result in great losses. The operational management of firefighting resources is essential in effective wildfire responses. This management is assisted by different optimisation techniques, which means that the development of suitable optimisation models is crucial. This document presents the study done on the use of different optimisation approaches and their appplicability in the prepositioning of resources under uncertainty. The uncertainty present in this problem is mainly represented by the wind and, in order to create scenarios that describe the uncertainty of the climate characteristics, we carried out a statistical analysis of real data obtained by a weather station near the case study area. The approaches studied were: stochastic optimisation, robust optimisation and distributionally robust optimisation. In the stochastic optimisation approach that was taken the expected burned area is minimized. The robust optimisation approach prepositions the firefighting resources such that the burned area in the worst-case is minimized. Lastly, the distributionally robust optimisation approach includes traits from both of the previous techniques by minimizing the impact of the worst-case probability distribution (of the scenarios). In order to perform computational tests, we generated instances based on real data and the scenarios obtained by the statistical analysis. For every model, we were able to obtain the location of the optimal prepositioning for the firefighting resources. |
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Prepositioning of resources for fire suppression problem under uncertaintyWildfireFirefighting problemUncertaintyPrepositioningOptimisationWind modellingWildfires are natural events that, in recent decades, have been increasingly more frequent and severe, and when not contained, can result in great losses. The operational management of firefighting resources is essential in effective wildfire responses. This management is assisted by different optimisation techniques, which means that the development of suitable optimisation models is crucial. This document presents the study done on the use of different optimisation approaches and their appplicability in the prepositioning of resources under uncertainty. The uncertainty present in this problem is mainly represented by the wind and, in order to create scenarios that describe the uncertainty of the climate characteristics, we carried out a statistical analysis of real data obtained by a weather station near the case study area. The approaches studied were: stochastic optimisation, robust optimisation and distributionally robust optimisation. In the stochastic optimisation approach that was taken the expected burned area is minimized. The robust optimisation approach prepositions the firefighting resources such that the burned area in the worst-case is minimized. Lastly, the distributionally robust optimisation approach includes traits from both of the previous techniques by minimizing the impact of the worst-case probability distribution (of the scenarios). In order to perform computational tests, we generated instances based on real data and the scenarios obtained by the statistical analysis. For every model, we were able to obtain the location of the optimal prepositioning for the firefighting resources.Incêndios florestais são eventos naturais que, em décadas recentes, têm vindo a ser mais frequentes e mais severos e, quando não contidos, podem resultar em grandes prejuízos. Uma boa gestão de recursos de combate aos incêndios é importante para a eficácia da sua supressão. Esta gestão pode ser assistida por diferentes técnicas de otimização, o que leva a uma grande importância no desenvolvimento das mesmas. Este documento apresenta o estudo feito sobre o uso de diferentes técnicas de otimização e a sua aplicabilidade no pré-posicionamento dos recursos de combate a incêndio sob incerteza. A incerteza presente neste problema é principalmente resultante das condições do vento e, de forma a criar cenários que representem a incerteza das condições metereológicas, foi realizada uma análise estatística de dados reais obtidos por uma estação metereológica perto da zona de estudo. As técnicas de otimização utilizadas foram: optimização estocástica, optimização robusta e optimização robusta em distribuição. Na otimização estocástica é minimizado o valor esperado da área ardida. A otimização robusta determina o pré-posicionamento dos recursos que minimiza a área ardida no pior caso. Finalmente, a otimização robusta em distribuição inclui características de ambos minimizando o impacto da probabilidade de distribuição (dos cenários) do pior caso. De forma a realizar testes computacionais, foram geradas instâncias com base em dados reais e com informação obtida do estudo estatístico. Foi possível, para cada modelo, obter o local ótimo de pré-posicionamento dos recursos de combate ao incêndio.2024-11-08T13:37:20Z2024-07-19T00:00:00Z2024-07-19info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10773/42762engMarques, Francisco José Coelhoinfo:eu-repo/semantics/openAccessreponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2024-11-11T01:47:47Zoai:ria.ua.pt:10773/42762Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T19:13:09.428332Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse |
| dc.title.none.fl_str_mv |
Prepositioning of resources for fire suppression problem under uncertainty |
| title |
Prepositioning of resources for fire suppression problem under uncertainty |
| spellingShingle |
Prepositioning of resources for fire suppression problem under uncertainty Marques, Francisco José Coelho Wildfire Firefighting problem Uncertainty Prepositioning Optimisation Wind modelling |
| title_short |
Prepositioning of resources for fire suppression problem under uncertainty |
| title_full |
Prepositioning of resources for fire suppression problem under uncertainty |
| title_fullStr |
Prepositioning of resources for fire suppression problem under uncertainty |
| title_full_unstemmed |
Prepositioning of resources for fire suppression problem under uncertainty |
| title_sort |
Prepositioning of resources for fire suppression problem under uncertainty |
| author |
Marques, Francisco José Coelho |
| author_facet |
Marques, Francisco José Coelho |
| author_role |
author |
| dc.contributor.author.fl_str_mv |
Marques, Francisco José Coelho |
| dc.subject.por.fl_str_mv |
Wildfire Firefighting problem Uncertainty Prepositioning Optimisation Wind modelling |
| topic |
Wildfire Firefighting problem Uncertainty Prepositioning Optimisation Wind modelling |
| description |
Wildfires are natural events that, in recent decades, have been increasingly more frequent and severe, and when not contained, can result in great losses. The operational management of firefighting resources is essential in effective wildfire responses. This management is assisted by different optimisation techniques, which means that the development of suitable optimisation models is crucial. This document presents the study done on the use of different optimisation approaches and their appplicability in the prepositioning of resources under uncertainty. The uncertainty present in this problem is mainly represented by the wind and, in order to create scenarios that describe the uncertainty of the climate characteristics, we carried out a statistical analysis of real data obtained by a weather station near the case study area. The approaches studied were: stochastic optimisation, robust optimisation and distributionally robust optimisation. In the stochastic optimisation approach that was taken the expected burned area is minimized. The robust optimisation approach prepositions the firefighting resources such that the burned area in the worst-case is minimized. Lastly, the distributionally robust optimisation approach includes traits from both of the previous techniques by minimizing the impact of the worst-case probability distribution (of the scenarios). In order to perform computational tests, we generated instances based on real data and the scenarios obtained by the statistical analysis. For every model, we were able to obtain the location of the optimal prepositioning for the firefighting resources. |
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2024 |
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2024-11-08T13:37:20Z 2024-07-19T00:00:00Z 2024-07-19 |
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info:eu-repo/semantics/publishedVersion |
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info:eu-repo/semantics/masterThesis |
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masterThesis |
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publishedVersion |
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http://hdl.handle.net/10773/42762 |
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http://hdl.handle.net/10773/42762 |
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eng |
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eng |
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info:eu-repo/semantics/openAccess |
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openAccess |
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