Probabilistic Wildfire Spread Prediction Using an Autoregressive Conditional Generative Adversarial Network

2025-11-25

AI Summary

Frequent wildfires intensify the urgent need for real-time, high-precision fire spread forecasting; however, physics-based simulators suffer from prohibitive computational cost, while existing deep learning models often produce over-smoothed predictions that fail to capture dynamic fire-front evolution and inherent uncertainty. To address this, we propose an autoregressive conditional generative adversarial network (AR-CGAN) that explicitly incorporates meteorological and topographic priors to model the spatiotemporal nonlinear transition of fire states. Leveraging adversarial training, our framework enhances fire-front sharpness and enables calibrated probabilistic forecasting, achieving a balance between physical interpretability and long-term predictive stability. Extensive experiments demonstrate that the proposed method significantly outperforms state-of-the-art deep learning baselines in both overall prediction accuracy and fire-front boundary delineation, delivering more reliable and operationally actionable spatiotemporal forecasts for emergency response and evacuation planning.

Technology Category

• Computer Vision: Generative Adversarial Networks (GANs) for Vision
• Machine Learning: Deep Generative Models & Autoencoders
• Multiagent Systems: Adversarial Agents

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

Abstract

Climate change has intensified the frequency and severity of wildfires, making rapid and accurate prediction of fire spread essential for effective mitigation and response. Physics-based simulators such as FARSITE offer high-fidelity predictions but are computationally intensive, limiting their applicability in real-time decision-making, while existing deep learning models often yield overly smooth predictions that fail to capture the complex, nonlinear dynamics of wildfire propagation. This study proposes an autoregressive conditional generative adversarial network (CGAN) for probabilistic wildfire spread prediction. By formulating the prediction task as an autoregressive problem, the model learns sequential state transitions, ensuring long-term prediction stability. Experimental results demonstrate that the proposed CGAN-based model outperforms conventional deep learning models in both overall predictive accuracy and boundary delineation of fire perimeters. These results demonstrate that adversarial learning allows the model to capture the strong nonlinearity and uncertainty of wildfire spread, instead of simply fitting the pixel average. Furthermore, the autoregressive framework facilitates systematic temporal forecasting of wildfire evolution. The proposed CGAN-based autoregressive framework enhances both the accuracy and physical interpretability of wildfire spread prediction, offering a promising foundation for time-sensitive response and evacuation planning.

Problem

• Predicting wildfire spread rapidly and accurately for mitigation
• Overcoming computational intensity of physics-based simulators like FARSITE
• Capturing nonlinear dynamics and uncertainty in fire propagation

Innovation

• Autoregressive conditional GAN for wildfire prediction
• Learns sequential state transitions for stability
• Adversarial learning captures nonlinear spread dynamics