Advanced Global Wildfire Activity Modeling with Hierarchical Graph ODE

2026-01-04

arXiv.org

AI Summary

This study addresses the challenge of modeling global wildfire activity, which is governed by coupled multi-scale atmosphere–ocean–land processes over long durations and large spatial extents. To this end, the authors propose a Hierarchical Graph ODE framework that uniquely integrates multi-level graph structures with Neural Ordinary Differential Equations (Neural ODEs). The approach employs an adaptive filtering message-passing mechanism to effectively fuse cross-scale information and leverages graph neural networks to parameterize the ODE, thereby learning the continuous-time dynamical evolution of wildfires. Experiments on the SeasFire Cube dataset demonstrate that the proposed method substantially outperforms existing models, achieving higher accuracy in long-horizon forecasting and producing continuous-time outputs that closely align with observational data.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Reasoning under Uncertainty: Graphical Models
• Computer Vision: Multi-modal Vision

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Wildfires, as an integral component of the Earth system, are governed by a complex interplay of atmospheric, oceanic, and terrestrial processes spanning a vast range of spatiotemporal scales. Modeling their global activity on large timescales is therefore a critical yet challenging task. While deep learning has recently achieved significant breakthroughs in global weather forecasting, its potential for global wildfire behavior prediction remains underexplored. In this work, we reframe this problem and introduce the Hierarchical Graph ODE (HiGO), a novel framework designed to learn the multi-scale, continuous-time dynamics of wildfires. Specifically, we represent the Earth system as a multi-level graph hierarchy and propose an adaptive filtering message passing mechanism for both intra- and inter-level information flow, enabling more effective feature extraction and fusion. Furthermore, we incorporate GNN-parameterized Neural ODE modules at multiple levels to explicitly learn the continuous dynamics inherent to each scale. Through extensive experiments on the SeasFire Cube dataset, we demonstrate that HiGO significantly outperforms state-of-the-art baselines on long-range wildfire forecasting. Moreover, its continuous-time predictions exhibit strong observational consistency, highlighting its potential for real-world applications.

Problem

• wildfire modeling
• global wildfire activity
• long-range forecasting
• multi-scale dynamics
• continuous-time prediction

Innovation

• Hierarchical Graph ODE
• Neural ODE
• multi-scale modeling
• wildfire forecasting
• adaptive message passing