Causal Graph Neural Networks for Wildfire Danger Prediction (2403.08414v1)

Abstract: Wildfire forecasting is notoriously hard due to the complex interplay of different factors such as weather conditions, vegetation types and human activities. Deep learning models show promise in dealing with this complexity by learning directly from data. However, to inform critical decision making, we argue that we need models that are right for the right reasons; that is, the implicit rules learned should be grounded by the underlying processes driving wildfires. In that direction, we propose integrating causality with Graph Neural Networks (GNNs) that explicitly model the causal mechanism among complex variables via graph learning. The causal adjacency matrix considers the synergistic effect among variables and removes the spurious links from highly correlated impacts. Our methodology's effectiveness is demonstrated through superior performance forecasting wildfire patterns in the European boreal and mediterranean biome. The gain is especially prominent in a highly imbalanced dataset, showcasing an enhanced robustness of the model to adapt to regime shifts in functional relationships. Furthermore, SHAP values from our trained model further enhance our understanding of the model's inner workings.

• The paper introduces a novel causal GNN framework that employs a causal adjacency matrix and PCMCI for precise wildfire forecasting.
• The methodology leverages LSTM layers for temporal feature extraction and convolutional layers to model true causal interactions among variables.
• Experimental results on the SeasFire Datacube show the model's robustness, outperforming conventional LSTM, GRU, and non-causal GNN variants.

Causal Graph Neural Networks Enhance Wildfire Danger Prediction

Introduction

Wildfire forecasting presents a complex challenge due to the intricate interplay among various factors such as weather conditions, vegetation types, and human activities. Recent advancements in deep learning (DL) have opened avenues for accurately predicting wildfire incidents by learning directly from large datasets. The paper by Zhao et al. builds upon these advancements by integrating causality with Graph Neural Networks (GNNs), aiming to model the causal mechanisms among variables explicitly. This approach not only seeks to predict wildfire patterns with high accuracy but also ensures that prediction models are grounded in the underlying processes driving wildfires.

Methodology

At the core of their methodology is the construction of a causal adjacency matrix that defines the relationships among different variables influencing wildfire incidents. This matrix enables the removal of spurious links and focuses on the synergistic effects that truly matter in fire prediction. The variables considered include local weather conditions and Oceanic and Climatic Indices (OCIs), each playing a known role in influencing wildfire likelihood. The paper leverages the PCMCI method for causal discovery, ensuring that the links established among variables are based on sound statistical inference.

The GNN framework incorporates these causally connected variables as nodes in a graph, where the graph's edges are determined by the causal adjacency matrix. An LSTM layer extracts temporal features for each variable, serving as node features. These are then refined through a series of convolutional layers, allowing the model to capture and update the information flow among variables effectively.

Experimental Results

The validation of the proposed methodology was carried out on the SeasFire Datacube, focusing on European regions known for their susceptibility to wildfires. The causal GNN demonstrated superiority in forecasting wildfire patterns, particularly in the highly imbalanced dataset scenario, signifying the model's robustness. This was especially pronounced when compared to baselines like LSTM, GRU, and other GNN variants that do not incorporate causal structures explicitly.

Implications and Future Directions

The paper's findings have significant practical and theoretical implications. From a practical standpoint, enhancing wildfire danger prediction accuracy is crucial for timely interventions and resource allocation in vulnerable regions. Theoretically, the application of causality in DL models, specifically in GNNs, opens up new pathways for research in environmental science and disaster management.

Future research directions could explore dynamic causal models that accommodate temporal changes in causal relationships, potentially improving long-term forecasting abilities. Another area of interest would be extending this methodology to other types of natural disasters where similar complex interplays among variables exist.

Conclusion

Zhao et al.’s research on integrating causal inference with GNNs for wildfire danger prediction represents a significant step forward in leveraging AI for environmental challenges. By grounding model predictions in causally relevant relationships among variables, this approach not only improves prediction accuracy but also contributes to a deeper understanding of wildfire dynamics, offering a robust tool for disaster preparedness and response strategies.