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.

通过图神经网络将原因与复杂变量之间的因果机制显式建模，提高了欧洲北方和地中海生物群系野火模式预测的性能。该方法在高度不平衡的数据集中表现出卓越的性能，并增强了模型适应功能关系变化的稳健性。通过训练模型得到的 SHAP 值进一步增强了我们对模型内在工作原理的理解。

因果图神经网络用于野火危险预测

Causal Graph Neural Networks for Wildfire Danger Prediction

Wildfire forecasting problems usually rely on complex grid-based mathematical
models, mostly involving Computational fluid dynamics(CFD) and Celluar
Automata, but these methods have always been computationally expensive and
difficult to deliver a fast decision pattern. In this paper, we provide machine
learning based approaches that solve the problem of high computational effort
and time consumption. This paper predicts the burning duration of a known
wildfire by RF(random forest), KNN, and XGBoost regression models and also
image-based, like CNN and Encoder. Model inputs are based on the map of
landscape features provided by satellites and the corresponding historical fire
data in this area. This model is trained by happened fire data and landform
feature maps and tested with the most recent real value in the same area. By
processing the input differently to obtain the optimal outcome, the system is
able to make fast and relatively accurate future predictions based on landscape
images of known fires.

用机器学习方法预测森林火灾持续时间，通过卫星提供的地貌特征地图和历史火灾数据训练模型，并基于已知火灾的地貌图像进行快速准确的未来预测。