An Emulation Framework for Fire Front Spread
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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.
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.
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6 of 11 references.
References · 6
- 2014Adam: A Method for Stochastic Optimizationcited by 37,240
- 2019Generalizing from a Few Examples: A Survey on Few-Shot Learningcited by 463
- 2020Building high accuracy emulators for scientific simulations with deep neural architecture searchcited by 36
- 2020Convolutional LSTM Neural Networks for Modeling Wildland Fire Dynamicscited by 11
- 2020Designing Accurate Emulators for Scientific Processes using Calibration-Driven Deep Modelscited by 8
- 2019From Patch to Image Segmentation using Fully Convolutional Networks -- Application to Retinal Imagescited by 5
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- 2026Spatiotemporal Wildfire Prediction and Reinforcement Learning for Helitack Suppression
- 2026Robust Wildfire Forecasting under Partial Observability: From Reconstruction to Prediction ↗
- 2024Wildfire Autonomous Response and Prediction Using Cellular Automata (WARP-CA)
- 2026Adaptive Monitoring of Stochastic Fire Front Processes via Information-seeking Predictive Control ↗