# An Emulation Framework for Fire Front Spread

arXiv: https://arxiv.org/abs/2203.12160 · HTML: https://mlsanity.com/paper/2203.12160/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 11 references that are in the map.
- First posted on arXiv 23 Mar 2022.

## Builds on (most cited first, 6 of 6)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Generalizing from a Few Examples: A Survey on Few-Shot Learning](https://mlsanity.com/paper/1904.05046/) (2019)
- [Building high accuracy emulators for scientific simulations with deep neural architecture search](https://mlsanity.com/paper/2001.08055/) (2020)
- [Convolutional LSTM Neural Networks for Modeling Wildland Fire Dynamics](https://mlsanity.com/paper/2012.06679/) (2020)
- [Designing Accurate Emulators for Scientific Processes using Calibration-Driven Deep Models](https://mlsanity.com/paper/2005.02328/) (2020)
- [From Patch to Image Segmentation using Fully Convolutional Networks -- Application to Retinal Images](https://mlsanity.com/paper/1904.03892/) (2019)

## Abstract (arXiv)

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.