用分层扩散模型提升长期野火风险预测效率与精度
N-Tree Diffusion for Long-Horizon Wildfire Risk Forecasting
- 共享早期去噪阶段，后期分叉实现多时步预测
- 相比基线方法推理成本更低，预测精度更稳定
- 适合需要高效长时序风险预测的应用场景
长期野火风险预测需在事件稀疏监督下生成概率性空间场，并保持多预测时步的计算效率。将扩散模型扩展至多步预测通常对每个时间步独立重复去噪过程，造成冗余计算。本文提出层级扩散模型 N-Tree Diffusion（NT-Diffusion），将火灾发生表示为连续的火险图（FRMs），提供适合概率建模的平滑空间风险场。不为每个预测时刻单独运行扩散轨迹，而是共享早期去噪阶段，并在后期分叉以实现时步特异性优化，同时减少冗余采样。我们在一个新构建的真实世界野火数据集上评估该框架，结果表明，在长期概率预测任务中，NT-Diffusion 在保持一致精度提升的同时，显著降低了推理开销。
原文摘要 · Abstract (English)
Long-horizon wildfire risk forecasting requires generating probabilistic spatial fields under sparse event supervision while maintaining computational efficiency across multiple prediction horizons. Extending diffusion models to multi-step forecasting typically repeats the denoising process independently for each horizon, leading to redundant computation. We introduce N-Tree Diffusion (NT-Diffusion), a hierarchical diffusion model designed for long-horizon wildfire risk forecasting. Fire occurrences are represented as continuous Fire Risk Maps (FRMs), which provide a smoothed spatial risk field suitable for probabilistic modeling. Instead of running separate diffusion trajectories for each predicted timestamp, NT-Diffusion shares early denoising stages and branches at later levels, allowing horizon-specific refinement while reducing redundant sampling. We evaluate the proposed framework on a newly collected real-world wildfire dataset constructed for long-horizon probabilistic prediction. Results indicate that NT-Diffusion achieves consistent accuracy improvements and reduced inference cost compared to baseline forecasting approaches.
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