Publicação
Minimizing the impact of forest fires
| Resumo: | Forest fires are a growing environmental, social and economic threat, especially in regions with high vegetation density and frequent ignition occurrences. This thesis addresses the problem of optimizing the allocation of firefighting resources to minimize the impact of forest fires, focusing on scenarios with multiple simultaneous ignitions and limited resources. The research proposes and evaluates several approaches, including single-objective, lexicographic, and multi-objective optimization formulations, to support strategic decision-making in fire suppression. First, a constructive heuristic is developed to quickly generate feasible initial solutions to the firefighting resource allocation and routing problem. Then, metaheuristics, such as the genetic algorithm and differential evolution algorithm are implemented to explore complex and large-scale solution spaces effectively. The proposed methods are validated using a case-study from the Braga district, Portugal, a region characterized by recurrent and severe forest fires. Several operational scenarios are studied, considering different levels of resource availability. The models aim to minimize the number of fires extinguished after 90 minutes, the total burned area, the time required to extinguish all fires, the total used water and the number of firefighting resources needed to extinguish all fires. The results show that metaheuristic-based approaches are efficient in terms of solution quality and adaptability under resource-limited conditions. In particular, the multi-objective solutions allows the decision-maker to identify the compromise solutions and choose according to his/her preferences. This thesis contributes to the development of advanced optimization models for forest fire suppression and presents a computational implementation that can serve as a decision support tool for civil protection entities. The findings highlight the importance of integrating smart resource allocation strategies into operational response plans to increase resilience and reduce the impacts of forest fires, especially in the face of increasing challenges posed by climate change. |
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| Autores principais: | Matos, Marina A. |
| Assunto: | Forest fire suppression Optimization Heuristics Metaheuristics Resource allocation Supressão de incêndios florestais Otimização Heurísticas Metaheurísticas Alocação de recursos |
| Ano: | 2025 |
| País: | Portugal |
| Tipo de documento: | tese de doutoramento |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Universidade do Minho |
| Idioma: | inglês |
| Origem: | RepositóriUM - Universidade do Minho |
| Resumo: | Forest fires are a growing environmental, social and economic threat, especially in regions with high vegetation density and frequent ignition occurrences. This thesis addresses the problem of optimizing the allocation of firefighting resources to minimize the impact of forest fires, focusing on scenarios with multiple simultaneous ignitions and limited resources. The research proposes and evaluates several approaches, including single-objective, lexicographic, and multi-objective optimization formulations, to support strategic decision-making in fire suppression. First, a constructive heuristic is developed to quickly generate feasible initial solutions to the firefighting resource allocation and routing problem. Then, metaheuristics, such as the genetic algorithm and differential evolution algorithm are implemented to explore complex and large-scale solution spaces effectively. The proposed methods are validated using a case-study from the Braga district, Portugal, a region characterized by recurrent and severe forest fires. Several operational scenarios are studied, considering different levels of resource availability. The models aim to minimize the number of fires extinguished after 90 minutes, the total burned area, the time required to extinguish all fires, the total used water and the number of firefighting resources needed to extinguish all fires. The results show that metaheuristic-based approaches are efficient in terms of solution quality and adaptability under resource-limited conditions. In particular, the multi-objective solutions allows the decision-maker to identify the compromise solutions and choose according to his/her preferences. This thesis contributes to the development of advanced optimization models for forest fire suppression and presents a computational implementation that can serve as a decision support tool for civil protection entities. The findings highlight the importance of integrating smart resource allocation strategies into operational response plans to increase resilience and reduce the impacts of forest fires, especially in the face of increasing challenges posed by climate change. |
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