# A Probabilistic Approach to Wildfire Spread Prediction Using a Denoising Diffusion Surrogate Model

arXiv: https://arxiv.org/abs/2507.00761 · HTML: https://mlsanity.com/paper/2507.00761/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Placed in Flow matching, time series and science, one of 14 directions in Diffusion models.
- Builds on 25 of its 71 references that are in the map.
- First posted on arXiv 1 Jul 2025.

## Placed in

- Diffusion models / [Flow matching, time series and science](https://mlsanity.com/diffusion/flow-matching-time-series-and-science/)

## Builds on (most cited first, 25 of 25)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [Structured Denoising Diffusion Models in Discrete State-Spaces](https://mlsanity.com/paper/2107.03006/) (2021)
- [Analog Bits: Generating Discrete Data using Diffusion Models with Self-Conditioning](https://mlsanity.com/paper/2208.04202/) (2022)
- [WeatherBench: A benchmark dataset for data-driven weather forecasting](https://mlsanity.com/paper/2002.00469/) (2020)
- [VEEGAN: Reducing Mode Collapse in GANs using Implicit Variational Learning](https://mlsanity.com/paper/1705.07761/) (2017)
- [GenCast: Diffusion-based ensemble forecasting for medium-range weather](https://mlsanity.com/paper/2312.15796/) (2023)
- [PreDiff: Precipitation Nowcasting with Latent Diffusion Models](https://mlsanity.com/paper/2307.10422/) (2023)
- [Gaussian Process Prior Variational Autoencoders](https://mlsanity.com/paper/1810.11738/) (2018)
- [A review of machine learning applications in wildfire science and management](https://mlsanity.com/paper/2003.00646/) (2020)
- [Generative Adversarial Networks (GANs Survey): Challenges, Solutions, and Future Directions](https://mlsanity.com/paper/2005.00065/) (2020)
- [Latent diffusion models for generative precipitation nowcasting with accurate uncertainty quantification](https://mlsanity.com/paper/2304.12891/) (2023)
- [On the Limitations of Multimodal VAEs](https://mlsanity.com/paper/2110.04121/) (2021)
- [A Denoising Diffusion Model for Fluid Field Prediction](https://mlsanity.com/paper/2301.11661/) (2023)
- [Appa: Bending Weather Dynamics with Latent Diffusion Models for Global Data Assimilation](https://mlsanity.com/paper/2504.18720/) (2025)
- [Forecasting Tropical Cyclones with Cascaded Diffusion Models](https://mlsanity.com/paper/2310.01690/) (2023)
- [Turbulence Scaling from Deep Learning Diffusion Generative Models](https://mlsanity.com/paper/2311.06112/) (2023)
- [Comparative Analysis of Generative Models: Enhancing Image Synthesis with VAEs, GANs, and Stable Diffusion](https://mlsanity.com/paper/2408.08751/) (2024)
- [Improving VAE generations of multimodal data through data-dependent conditional priors](https://mlsanity.com/paper/1911.10885/) (2019)
- [Generating High-Resolution Regional Precipitation Using Conditional Diffusion Model](https://mlsanity.com/paper/2312.07112/) (2023)
- [Latent Diffusion Model for Generating Ensembles of Climate Simulations](https://mlsanity.com/paper/2407.02070/) (2024)

## Built on by (most cited first, 11 of 11)

- [Neural-Parameterized Cellular Automata for Wildfire Spread](https://mlsanity.com/paper/2606.11676/) (2026)
- [WildfireSpreadBench: The Metric Decides the Model in Wildfire Spread Prediction](https://mlsanity.com/paper/2609.22191/) (2026)
- [Probabilistic Wildfire Spread Prediction Using an Autoregressive Conditional Generative Adversarial Network](https://mlsanity.com/paper/2511.21019/) (2025)
- [Diffusion Posterior Sampler for Hyperspectral Unmixing with Spectral Variability Modeling](https://mlsanity.com/paper/2512.09871/) (2025)
- [Trustworthy Data-Driven Wildfire Risk Prediction and Understanding in Western Canada](https://mlsanity.com/paper/2601.01677/) (2026)
- [Large-Scale 3D Ground-Motion Synthesis with Physics-Inspired Latent Operator Flow Matching](https://mlsanity.com/paper/2603.17403/) (2026)
- [Wildfire Spread Scenarios: Increasing Sample Diversity of Segmentation Diffusion Models with Training-Free Methods](https://mlsanity.com/paper/2603.20188/) (2026)
- [DiffUNet^2: Bidirectional Conditional Diffusion for Probabilistic Scientific Spatiotemporal Modeling](https://mlsanity.com/paper/2606.03926/) (2026)
- [Multi-Agent Reinforcement Learning for Autonomous UAV Exploration in Wildfire Response](https://mlsanity.com/paper/2609.10433/) (2026)
- [Interpretable Patch-Based Deep Learning for Wildfire Spread Prediction from Ensemble Simulations](https://mlsanity.com/paper/2609.18555/) (2026)
- [FireGraphNet: Sub-daily wildfire spread prediction with directed graph neural networks](https://mlsanity.com/paper/2610.19191/) (2026)

## Abstract (arXiv)

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.