From Insight to Action: Prescriptive Analytics in FiSci’s Mitigate Tool
Building the Optimal Fire Risk Strategy
Once you understand the fire risk across your landscape (descriptive analytics) and model where and how fires are likely to spread (predictive analytics), the final step is answering the most important question:
“What should we do about it?”
This is where prescriptive analytics come into play — empowering land managers, planners, and infrastructure owners to choose the best mitigation strategies, backed by data.
What Are Prescriptive Analytics?
Prescriptive analytics guide users in making decisions by evaluating different intervention options and predicting their outcomes. In FiSci’s Mitigate tool, this means building, testing and refining fire treatment strategies — helping ensure that every action taken is informed, targeted and effective.
Where descriptive analytics show you the state of the land, and predictive analytics show where fire might go, prescriptive analytics offer recommendations and validation for action.
Treatment Strategies in Mitigate
FiSci’s Mitigate tool allows users to plan and compare a range of bushfire mitigation treatments, including:
🔥 Prescribed burning
Reduce fuel load in strategic locations with controlled burns.🪓 Mechanical intervention
Remove or reduce vegetation using slashing, thinning or other mechanical means.🐄 Grazing
Use livestock to manage fine fuels such as grass and leaf litter, especially in transitional zones.🌱 Revegetation or green firebreaks
Establish low-flammability vegetation or natural buffers that can slow or redirect fire spread.
These treatments can be mapped, sequenced and prioritised within Mitigate, allowing land managers to coordinate both immediate actions and long-term strategies across a property or region.
Testing and Validating Strategies
What sets prescriptive analytics apart is the ability to test these strategies against predictive models — seeing how different treatments reduce fire risk over time and under different weather conditions.
This means users can answer questions like:
“If we implement a burn here, will it protect this asset?”
“Is mechanical clearing or grazing more effective in this zone?”
“How much risk reduction do we achieve in Year 1 vs Year 5?”
Users can also explore cost-benefit trade-offs, resource allocation, and seasonal constraints — allowing them to build a realistic, defensible plan that aligns with both ecological and operational objectives.
Short-Term Wins and Long-Term Resilience
Prescriptive analytics don’t just help plan the next season’s works — they support multi-year strategic planning to reduce long-term fire exposure. This is particularly valuable for:
Carbon project developers looking to reduce emissions from fire
Utility companies protecting critical infrastructure
Conservation groups managing fire-adapted landscapes
Local governments with community protection mandates
The outcome? Smarter, targeted treatments that are proactive instead of reactive, based on the landscape’s risk profile and the user's tolerance for exposure.
From Planning to Action
FiSci’s Mitigate tool provides users with a clear, interactive pathway from data to decision. It allows planners to:
✅ Develop actionable treatment plans
✅ Justify decisions with risk-reduction evidence
✅ Communicate plans with stakeholders
✅ Monitor progress against long-term goals
Prescriptive analytics bring it all together — connecting data, models and treatments to help users act with confidence and build bushfire resilience over time.
If you're ready to build your own treatment plan, or want to see how FiSci can support your bushfire mitigation efforts, contact us today for a demo.
