An Emulation Framework for Fire Front Spread
This addresses bushfire prevention and response efforts by providing faster and more reliable forecasts, though it appears incremental as an application of existing emulation techniques to this domain.
The paper tackles bushfire spread forecasting by developing a machine learning-based emulation framework that can closely reproduce simulated fire-front data, enabling fast generation of large prediction ensembles for more robust forecasts.
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.