792. Wildfire fuel management: robust optimisation of prescribed burning
Invited abstract in session HB-7: Analytics for Prescribed Burning and Firebreaks Location in Forest Fires Prevention, cluster Use of Analytics in Forest Fires Management.
Thursday, 10:30-12:00Room: CE-210
Authors (first author is the speaker)
| 1. | Dmytro Matsypura
|
| Discipline of Business Analytics, Business School, The University of Sydney | |
| 2. | Nam Ho-Nguyen
|
| The University of Sydney | |
| 3. | Tomas Lagos
|
| The University of Pittsburgh | |
| 4. | Oleg Prokopyev
|
| University of Pittsburgh |
Abstract
Wildfires are an integral part of many ecosystems; their severity has been worsening rapidly, however, over the past decade. The intensity and severity of wildfires can be reduced through fuel management activities - the most common and effective being prescribed burning. We propose a multi-period robust optimization framework based on mixed integer programming techniques to determine the optimal spatial allocation of prescribed burning activities over a finite planning horizon. We model fuel accumulation with Olson’s equation, and, in contrast with the existing literature, our formulation is linear, significantly improving scalability. To capture potential fire spread along with irregular landscape connectivity, we model the landscape as a graph and exploit graph connectivity measures (e.g. the number of connected components) as optimization objectives. Our computational experiments reveal interesting insights and demonstrate the advantages and limitations of the proposed approach.
Keywords
- Mixed Integer Programming
- Robust optimization
- Environmental Management
Status: accepted
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