N-Tree Diffusion for Long-Horizon Wildfire Risk Forecasting

TL;DR

This work introduces N-Tree Diffusion (NT-Diffusion), a hierarchical diffusion model designed for long-horizon wildfire risk forecasting that achieves consistent accuracy improvements and reduced inference cost compared to baseline forecasting approaches.

Abstract

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.

Figures (6)

• Figure 1: Illustration of the Fire Risk Map (FRM).(a) shows a single fire occurrence in the area and (b) presents its Gaussian risk representation. For multiple real fire events occurring in the region corresponding to the real map, as depicted in (c), the resulting FRM is generated as shown in (d).
• Figure 2: Two extreme computational structures for multi-horizon diffusion generation. (a) Fully shared reverse trajectory. (b) Fully independent reverse trajectories.
• Figure 3: Illustration of NT-Diffusion with $L=4$ hierarchical layers, where reverse trajectories are partially shared and progressively branched across horizons.
• Figure 4: Illustration of the diffusion denoising process at noise level $s$ and time $t$.
• Figure 5: Comparison of execution time (ms) among diffusion-based models with different steps.