Wildfire Response Command Centre

Predict fire-front spread rate, allocate ground and aerial crews, and plan community evacuation timing for a wildfire incident.

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Why this matters

Predict fire-front spread rate, allocate ground and aerial crews, and plan community evacuation timing for a wildfire incident.

This model is adapted from an internal scenario-planning tool, distilled here into three linked calculations that mirror how real operators, engineers and analysts reason about the system.

How the model works

Wind speed (km/h) — Fire-front spread rate (Rothermel-style approximation) against 23 t/ha fuel load and a 12° slope.
Ground crews deployed (ratio) — Containment coverage against an 800 ha target, combining ground crews (30 ha/day each) with 6 aerial units.
Public warning lead time (hr) — Evacuation adequacy for an 8,200-person community against 2,400 people/hr road capacity.

Reading the results

Each control drives one of three underlying formulas taken from the source engineering model. Moving a slider recomputes its metric instantly and updates the 3D bar in the simulation — taller, brighter bars mean the system is closer to its optimum operating envelope. Try pushing each parameter to its extreme to see where the model breaks down or saturates.

Frequently Asked Questions

What is wildfire response command centre used for?

Predict fire-front spread rate, allocate ground and aerial crews, and plan community evacuation timing for a wildfire incident.

Is this a real-world engineering model or a toy?

The underlying formulas are simplified versions of real planning heuristics used in this domain — accurate enough to show the right trends and trade-offs, but not a substitute for full engineering simulation software.

Can I use my own numbers?

Yes — every slider in the simulation maps directly onto one of the model's input variables, so you can explore scenarios well outside the defaults shown here.

Why does the bar height saturate at the extremes?

Each metric is normalised to a 0–1 range against a realistic reference ceiling from the source model, so very large inputs will visually cap out even though the underlying number keeps growing.