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3646. pyO3F - A python framework for wildfire related optimization
Invited abstract in session MB-12: OR in Forestry I, stream OR in Agriculture and Forestry .
Monday, 10:30-12:00Room: 13 (building: 116)
Authors (first author is the speaker)
1. | Filipe Alvelos
|
Department of Production and Systems, ALGORITMI Research Center / LASI, University of Minho | |
2. | Marco Henrique Vieira Marto
|
ALGORITMI Research Center / LASI, University of Minho, Portugal | |
3. | Isabel Martins
|
Center for Mathematics, Fundamental Applications and Operations Research, School of Agriculture, University of Lisbon | |
4. | Susete Marques
|
Forest Research Center, University of Lisbon, School of Agriculture | |
5. | André Bergsten Mendes
|
Departamento de Engenharia Naval e Oceânica, Universidade de São Paulo |
Abstract
We present a python framework, pyO3F, which aims to support the development of optimization approaches for wildfire related problems. We describe the pyO3F main modules, their interactions, and the information they use to model fire behaviour, fire spread scenarios, and different types of firefighting resources. At the core of pyO3F is the use of the minimum travel time principle in a network with nodes representing locations and arcs representing potential fire transmissions between adjacent locations. Fire spread is the modelled as a set of quickest paths with respect to the fire transmission times associated with the arcs (estimated with fire behaviour models).
We exemplify the use of pyO3F in two types of problems: fire suppression and fire-aware forest management. In the former, it is intended to manage a set of available firefighting resources to minimize the impact of fire (e.g. the burned area). In the latter, it is intended to select a set of prescriptions, one for each forest stand, to maximize the net present value of the timber, taking into account bounds on other ecosystem services (e.g. carbon stock) and fire spread simulations.
Keywords
- Software
- Forestry Management
- Programming, Mixed-Integer
Status: accepted
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