Modelling Risk Before Fire Strikes: Predictive Analytics in FiSci’s Mitigate Tool
Understanding Future Fire Behaviour
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.
At FiSci, our Mitigate tool uses predictive analytics to simulate how a fire is likely to behave under different conditions — helping users plan ahead and prioritise their mitigation actions with confidence.
What Are Predictive Analytics?
Predictive analytics leverage historical and real-time data to forecast outcomes based on variable inputs. In the context of bushfire management, this means modelling how a fire could ignite, spread, and threaten assets based on changing factors like fuel load, terrain, and weather.
FiSci’s predictive analytics are built on a powerful spatial engine that allows users to explore "what-if" scenarios — giving them insight into where future fires may come from, who or what is most at risk, and how fire may behave once ignited.
Key Predictive Layers in Mitigate
Our tool brings together predictive capabilities in three essential ways:
🔥 Risk From
This layer models where a fire is most likely to originate and pose a threat to a user’s area of interest. It helps answer the question:
“Where will danger come from?”
By simulating ignition likelihood and fire spread under different weather profiles, users can identify the high-risk zones outside their boundary that may influence internal fire risk.
🎯 Risk To
This insight focuses on what within the user’s land is most vulnerable to encroaching fire. It considers proximity to fuel loads, topography, asset locations, and simulated fire paths.
“What’s most at risk of being hit by fire — and how soon?”
This allows for intelligent asset prioritisation and strategic placement of fuel treatments or defensive measures.
🌬️ Fire Spread Modelling
FiSci’s predictive engine simulates how fire is likely to move across the landscape, based on user-defined variables such as:
Wind speed and direction
Temperature and humidity
Fuel moisture
Vegetation and slope
This allows landholders and planners to customise weather conditions and explore multiple future scenarios, adjusting treatment strategies accordingly.
Why Predictive Analytics Matter
In fire mitigation, timing and accuracy are everything. Predictive analytics help users:
Evaluate the effectiveness of current mitigation strategies
Prioritise areas for treatment before peak fire season
Simulate fire scenarios for planning, training, and policy-making
Reduce uncertainty and bias in risk planning
Communicate fire risk with evidence-based modelling
By enabling users to simulate and visualise fire dynamics, Mitigate turns static data into powerful forward-looking insight.
Bridging Descriptive and Prescriptive
Predictive analytics sit at the core of FiSci’s platform — acting as the bridge between understanding the landscape (descriptive) and making the right intervention choices (prescriptive).
It empowers decision-makers to not only react to bushfire risk, but to plan ahead with data-backed certainty.
In Part 3 of this series, we’ll explore how users can take these insights and apply real-world treatments and interventions to reduce risk over time.
Want to see Mitigate’s predictive modelling in action? Book a demo with our team or get in touch to explore how this can support your fire planning process.
