Stochastic optimization provides key to combatting increasing frequency and intensity of wildfires

When you take a look back, even just over the past couple of years, the devastation caused by the seemingly growing threat of large-scale wildfires has made headlines around the world. From the unprecedented brushfires in Australia in 2019-2020, to California’s record setting wildfire season later that same year, and most recently the Colorado prairie grass fires which occurred well outside of the traditional fire season, the increasing frequency and intensity of wildfire incidents are a growing global concern.

Not only are these fires capable of causing billions of dollars in damage each year, but contribute to the deaths of thousands of people, not to mention have a devastating impact on local wildlife and ecosystems.

But are there steps that can be taken to better prepare and position first responders and others involved in combatting and minimizing the physical and financial impact of these fires?

Joining me to discuss his work using stochastic optimization to support better decisions regarding resource deployment both in the stages before a fire occurs as well as when multiple fires are underway is Lewis Ntaimo with Texas A&M University.

The model also allowed [us] to identify and prioritize areas that were prone to fire occurrence and needed more resources. Because once you use the model, it can tell you where more resources are needed based on the data that you feed it. Because the model takes into account the fire occurrence in each area, the vegetation at that particular location, the available resources at fire operations bases nearby, and then prepositions the resources. So you could basically identify priority areas.

Interviewed this episode:

Lewis Ntaimo

Texas A&M University

Lewis Ntaimo is a professor and department head of Industrial & Systems Engineering, and the Sugar and Mike Barnes Department Head Chair with Texas A&M University.