Abstract
With climate change expected to exacerbate fire weather conditions, the accurate and timely anticipation of wildfires becomes increasingly crucial for disaster mitigation. In this study, we utilize SeasFire, a comprehensive global wildfire dataset with climate, vegetation, oceanic indices, and human-related variables, to enable seasonal wildfire forecasting with machine learning. For the predictive analysis, we present FireCastNet, a novel architecture which combines a 3D convolutional encoder with GraphCast, originally developed for global short-term weather forecasting using graph neural networks. FireCastNet is trained to capture the context leading to wildfires, at different spatial and temporal scales. Our investigation focuses on assessing the effectiveness of our model in predicting the presence of burned areas at varying forecasting time horizons globally, extending up to six months into the future, and on how different spatial or/and temporal context affects the performance. Our findings demonstrate the potential of deep learning models in seasonal fire forecasting; longer input time-series leads to more robust predictions, while integrating spatial information to capture wildfire spatio-temporal dynamics boosts performance. Finally, our results hint that in order to enhance performance at longer forecasting horizons, a larger receptive field spatially needs to be considered.
Abstract (translated)
随着气候变化预计会加剧火灾天气条件，准确及时地预测野火变得越来越重要，以减轻灾害影响。在本研究中，我们利用SeasFire这一包含气候、植被、海洋指数和人类相关变量的全球野火数据集，通过机器学习实现季节性野火预报。对于预测分析，我们提出了FireCastNet，一种结合了3D卷积编码器与GraphCast（最初用于使用图神经网络进行全球短期天气预报）的新架构。FireCastNet被训练以捕捉导致不同空间和时间尺度上野火发生的背景情况。 我们的研究重点在于评估模型在全球范围内预测不同预报时间段内的烧毁区域存在的有效性，这些时间段可以延长到六个月之后，并探讨不同的空间或/及时间背景如何影响性能。我们的发现展示了深度学习模型在季节性火灾预报中的潜力；较长的输入时间序列会导致更稳健的预测，而整合空间信息以捕捉野火的空间和时间动态则能提升表现。最后，我们的结果显示为了提高长时间段预报的效果，需要考虑更大的空间接受范围。
URL
https://arxiv.org/abs/2502.01550
