Modelling risk before fire strikes: predictive analytics in Mitigate
Part two of three. Where will danger come from, what is most at risk, and how will fire actually move?
Kevin Bonner
Once land managers and planners understand the current landscape and past fire behaviour through descriptive analytics, the next logical question is: what could happen next?
That’s where predictive analytics comes in. Mitigate uses it to simulate how a fire is likely to behave under different conditions, helping users plan ahead and prioritise mitigation with confidence.
What predictive analytics are
Predictive analytics leverage historical and real-time data to forecast outcomes based on variable inputs. In bushfire management that means modelling how a fire could ignite, spread and threaten assets based on changing factors like fuel load, terrain and weather.
Our predictive analytics are built on a spatial engine that lets users explore what-if scenarios — giving insight into where future fires may come from, who or what is most at risk, and how fire may behave once ignited.
The three predictive layers
Risk from
This layer models where a fire is most likely to originate and pose a threat to your area of interest. It answers: where will danger come from?
By simulating ignition likelihood and spread under different weather profiles, users can identify the high-risk zones outside their boundary that influence internal risk.
Risk to
This focuses on what within your land is most vulnerable to encroaching fire, considering proximity to fuel loads, topography, asset locations and simulated fire paths. It answers: what’s most at risk of being hit, and how soon?
That allows intelligent asset prioritisation and strategic placement of fuel treatments or defensive measures.
Fire spread modelling
The engine simulates how fire is likely to move across the landscape based on user-defined variables:
- Wind speed and direction
- Temperature and humidity
- Fuel moisture
- Vegetation and slope
Landholders and planners can customise weather conditions and explore multiple future scenarios, adjusting treatment strategies accordingly.
Why it matters
In fire mitigation, timing and accuracy are everything. Predictive analytics help users evaluate the effectiveness of current strategies, prioritise areas for treatment before peak season, simulate scenarios for planning and training, reduce uncertainty and bias in risk planning, and communicate risk with evidence-based modelling.
By enabling users to simulate and visualise fire dynamics, Mitigate turns static data into forward-looking insight.
Bridging descriptive and prescriptive
Predictive analytics sit at the core of the platform, acting as the bridge between understanding the landscape and making the right intervention choices. It lets decision-makers plan ahead with data-backed certainty rather than simply react.
