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

2025-07-01

AI Summary

Traditional wildfire spread prediction models struggle to characterize intrinsic uncertainty. To address this, we propose the first probabilistic wildfire spread forecasting framework based on Denoising Diffusion Probabilistic Models (DDPMs). Our method models fire evolution as a joint multi-scenario distribution and constrains the reverse diffusion process via a physics-informed loss function, enabling generation of diverse yet physically consistent future spread trajectories. Unlike deterministic approaches, our framework provides the first interpretable, sampleable probabilistic characterization of wildfire dynamics under environmental drivers. Experiments demonstrate that the predicted distributions exhibit high physical plausibility and scenario diversity, significantly improving situational awareness under complex topography and meteorological conditions. The framework thus delivers more robust decision support for emergency response operations.

Technology Category

• Reasoning under Uncertainty: Probabilistic Programming
• Computer Vision: Diffusion Models for Vision
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

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.

Problem

• Predict wildfire spread using generative AI
• Model uncertainty in fire dynamics with diffusion
• Generate ensemble forecasts for fire scenarios

Innovation

• Denoising diffusion model for wildfire prediction
• Generates ensembles of possible fire scenarios
• Accounts for uncertainty in wildfire dynamics