An Emulation Framework for Fire Front Spread

TL;DR

This paper introduces a machine learning-based emulation framework for simulating bushfire spread, enabling rapid and reliable ensemble forecasts of fire front dynamics based on observed data.

Contribution

It presents a novel emulation approach using machine learning to efficiently replicate complex fire spread simulations for improved forecasting.

Findings

Emulation closely reproduces simulated fire-front data.

Enables fast generation of multiple fire spread predictions.

Supports ensemble estimation for more robust forecasts.

Abstract

Forecasting bushfire spread is an important element in fire prevention and response efforts. Empirical observations of bushfire spread can be used to estimate fire response under certain conditions. These observations form rate-of-spread models, which can be used to generate simulations. We use machine learning to drive the emulation approach for bushfires and show that emulation has the capacity to closely reproduce simulated fire-front data. We present a preliminary emulator approach with the capacity for fast emulation of complex simulations. Large numbers of predictions can then be generated as part of ensemble estimation techniques, which provide more robust and reliable forecasts of stochastic systems.