Wildfire analytics: optimisation of fuel reduction

Abstract : Wildfires are a common phenomenon on most continents. They have occurred for an estimated 60 million years and are part of a regular climatic cycle. Nevertheless, wildfires represent a real and continuing problem that can have a significant impact on people, wildlife and the environment, especially since their severity has been worsening rapidly over the past decade. The intensity and severity of wildfires can be reduced through fuel reduction activities. The most common and effective fuel reduction activity is prescribed burning. We propose a multi-period optimization framework based on mixed integer programming (MIP) techniques to determine the optimal spatial allocation of prescribed burning activities over a finite planning horizon. 
In contrast to the existing literature, we model fuel accumulation with a modified Olson’s equation. To capture potential fire spread along with irregular landscape connectivity, we use a graph-theoretical approach that allows us to exploit graph connectivity measures as optimization objectives. The resulting mathematical programs can be tackled by general-purpose MIP solvers. Our computational experiments reveal interesting insights and demonstrate the advantages and limitations of the proposed approaches.
 
Bio : Dmytro Matsypura is an Associate Professor in the Discipline of Business Analytics at the University of Sydney Business School. He received his B.S. and M.S. degrees in Information Systems from Kyiv Polytechnic Institute (Ukraine) and a Ph.D. in Management Science from the University of Massachusetts Amherst. His current research focuses on applications of convex and combinatorial optimisation in forecasting, graph theory, transportation, and ecology.