Unit8 is committed to dedicating a portion of our working time to pro bono projects. We believe that by applying advanced technology to difficult problems, we can help socially responsible organisations maximise their impact.

Over the past year, we have collaborated with the World Wildlife Fund to help improve the prediction of wildfire spread in South America. In this blog post, the first of a series, we’ll describe how our team leveraged our artificial intelligence (AI) and software expertise to help address a complex and pressing problem.

Our hope is that by illustrating our thought process and project progress, we can provide others with useful insights into the datasets, methods, and an understanding of the state of the art when it comes to problems like modeling wildfires.

This post will cover:

  • The threats that wildfires pose in South America
  • How we translated the broad problem of wildfires into a more focused technical problem statement
  • Useful resources and a quick background on the state of the art when it comes to fire spread prediction technology
  • Useful datasets and data science methods for tackling this problem space


Wildfires can pose threats to ecosystems throughout the world, but in South America, the effects of deforestation can be particularly severe.

It’s impossible to overstate the global economic and environmental importance of forests. According to one research study, 41 million people worldwide hold jobs dependent on, or related to, the forest sector. At the same time, forest degradation is responsible for 15% of greenhouse gas emissions. That means large wildfires pose severe threats to national economies and climate objectives alike.

After a period of consultation with the WWF, the world’s largest conservation organisation, on where our technology might be most useful, Unit8 began a partnership with the organisation’s Bolivian Branch, WWF Bolivia, and FAN.

WWF Bolivia and FAN are generally able to accurately detect, track, and assess potential wildfire threats in near real time. They have a well-developed network of sensory devices and satellite images that help them identify early threats before they materialise. However, these organisations still face two key vulnerabilities: threats that arise from the unpredicted spread of fire inside the country, and threats that arise from fires that begin beyond Bolivia’s borders, where alerting systems may not be as rapid or accurate.

The danger stemming from unmanaged fire risk is enormous. For example, a devastating fire in 2019 burnt roughly 1 million hectares of forest in San Jose de Chiquitos. As such, organisations like WWF Bolivia and FAN constantly seek to improve their threat-modeling capabilities.

In partnering with WWF Bolivia and FAN, our key objective was to identify and implement technological solutions that could supplement their existing work in the detection, modeling, and elimination of wildfire threats.


Problem statement

Presented with the facts described in the previous section, we began our partnership by defining our overarching goal:
“Improve the reaction time in addressing fires that have already started”

Enabling a faster reaction time to a disaster that is already happening seems like a small improvement, but the consequences are significant and meaningful. Even a small advance in delivering information on how fire will spread or enter Bolivia significantly improves fire management capabilities.

Faster reaction times help firefighters prepare for wildfires. They help park rangers decide how best to deal with emerging threats. They allow national leaders to determine the best strategy and pick the right tradeoffs (e.g., which parts of endangered areas should be prioritized). Most importantly, faster reaction times help authorities decide which zones and communities should be evacuated, and when.

After we defined our primary goal, we determined that developing an accurate ML model for predicting fire spread could play a critical role in enabling faster reaction times, as well as identifying the factors that contributed the most to the spread of wildfires or wildfires. That knowledge, in turn, could help alleviate the impact of those factors.

Ultimately, we felt that improving reaction times would be most useful if our solution also provided tools for supporting decision-making. Thus, our two goals were to:

  • #1: Improve reaction times in fighting wildfires that had already started
  • #2: Provide statistical and visualization tools to support decision-making

We figured that Machine Learning would be an interesting approach for solving Problem #1, but we also knew that the problem needed a more concrete definition. In other words, we needed to narrow the problem to discrete prediction tasks.

After additional research and discussion, our team arrived at three sub-problems where we could apply ML techniques:

  • The prediction of the final area affected by a fire
  • The prediction of the rate of fire spread
  • The prediction of the direction of fire spread

We decided to focus on the latter two. In both cases, a successful solution would need to be able to answer the following: Knowing that a fire has started in a specific location in South America, can we predict where it is going to spread?

The first solution that came to mind was to design and implement an ML solution that could predict which direction of fire propagation was the most probable.
We knew that there are already surprisingly effective predictive models for hurricane path forecasts. However, we also determined that these models wouldn’t be as effective for wildfires. For one thing, wildfires can easily be started by humans, while tornados and hurricanes are purely meteorological phenomena. Moreover, fires are affected by a range of factors, including the presence of human beings, which may contribute to fires ceasing or, alternatively, growing significantly (e.g., when fuel is available). The problem is made even more complex by additional variables like topography and weather.

The dynamics of fire spread are also complex. In fact, a fire might be considered as a collection of separate physical processes. For instance, one factor in wildfires is spotting, which is the non-local creation of fires due to ignition caused by sparks and torches from a primary fire. Ignoring this factor can lead to inaccurate modeling.
We learned about spotting only after conducting extensive research on our own, and it is a good example of how it is essential to develop or account for domain knowledge when choosing the right ML method.

Pervious work

Below we present a short description of solutions that have already been investigated. Existing research can be grouped into two broad categories:

  • Mathematical modeling and simulation
  • Machine learning models

Mathematical models usually use a set of non-linear equations that are complex and involve a large amount of parameters. To work on them, specialized knowledge is required. These models are either physics-based or empirical.

Physics-based models are based on the analysis of the physical and chemical processes involved in fires. Two prominent models of this type are Nelson’s (2000) dead fuel moisture model and Albini’s (1979) spotting model which is used to estimate the maximum spotting distance. Nelson’s model is crucial for fire simulation because heat and moisture transfer in dead fuel is strongly related to ignition and fire spread potential. To be usable in practice, theoretical models need to be converted into finite-difference (computer-friendly) numerical ones.

Empirical models are derived from experiments and might be applied only in the environment similar to the one in which they were tested.
There are also models that merge the mentioned approaches. They are called semi-empirical and can simplify mathematical equations that describe physical phenomenons by making assumptions based on a statistical analysis of gathered experimental data. One widely used model in this category is Rothermel’s (1972) surface fire spread model.

The main limitation of these models is that they were very often crafted for specific locations or types of ecosystems. For example, in evaluating past approaches, we found a model for predicting behavior of crown fire designed specifically for Northern Rocky Mountains in the US, a model for predicting dead fuel moisture in Mediterranean areas, and a model for predicting dead fuel moisture in eucalyptus forests.
In short, empirical models and physical models often lack generalizability due to the complex nature of the modeled phenomena (in this case, fires).



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