A Probabilistic Approach to Wildfire Spread Prediction Using a Denoising Diffusion Surrogate Model
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866915877839962112 |
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| author | Yu, Wenbo Ghosh, Anirbit Finn, Tobias Sebastian Arcucci, Rossella Bocquet, Marc Cheng, Sibo |
| author_facet | Yu, Wenbo Ghosh, Anirbit Finn, Tobias Sebastian Arcucci, Rossella Bocquet, Marc Cheng, Sibo |
| contents | Thanks to recent advances in generative AI, computers can now simulate realistic and complex natural processes. We apply this capability to predict how wildfires spread, a task made difficult by the unpredictable nature of fire and the variety of environmental conditions it depends on. In this study, We present the first denoising diffusion model for predicting wildfire spread, a new kind of AI framework that learns to simulate fires not just as one fixed outcome, but as a range of possible scenarios. By doing so, it accounts for the inherent uncertainty of wildfire dynamics, a feature that traditional models typically fail to represent. Unlike deterministic approaches that generate a single prediction, our model produces ensembles of forecasts that reflect physically meaningful distributions of where fire might go next. This technology could help us develop smarter, faster, and more reliable tools for anticipating wildfire behavior, aiding decision-makers in fire risk assessment and response planning. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2507_00761 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Probabilistic Approach to Wildfire Spread Prediction Using a Denoising Diffusion Surrogate Model Yu, Wenbo Ghosh, Anirbit Finn, Tobias Sebastian Arcucci, Rossella Bocquet, Marc Cheng, Sibo Machine Learning Thanks to recent advances in generative AI, computers can now simulate realistic and complex natural processes. We apply this capability to predict how wildfires spread, a task made difficult by the unpredictable nature of fire and the variety of environmental conditions it depends on. In this study, We present the first denoising diffusion model for predicting wildfire spread, a new kind of AI framework that learns to simulate fires not just as one fixed outcome, but as a range of possible scenarios. By doing so, it accounts for the inherent uncertainty of wildfire dynamics, a feature that traditional models typically fail to represent. Unlike deterministic approaches that generate a single prediction, our model produces ensembles of forecasts that reflect physically meaningful distributions of where fire might go next. This technology could help us develop smarter, faster, and more reliable tools for anticipating wildfire behavior, aiding decision-makers in fire risk assessment and response planning. |
| title | A Probabilistic Approach to Wildfire Spread Prediction Using a Denoising Diffusion Surrogate Model |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2507.00761 |