Predicting the extent of massive wildfires once ignited is essential to reduce the subsequent socioeconomic losses and environmental damage, but challenging because of the complexity of fire behaviour. Existing physics-based models are limited in predicting large or long-duration wildfire events. Here, we develop a deep-learning-based predictive model, Fire-Image-DenseNet (FIDN), that uses spatial features derived from both near real-time and reanalysis data on the environmental and meteorological drivers of wildfire. We trained and tested this model using more than 300 individual wildfires that occurred between 2012 and 2019 in the western US. In contrast to existing models, the performance of FIDN does not degrade with fire size or duration. Furthermore, it predicts final burnt area accurately even in very heterogeneous landscapes in terms of fuel density and flammability. The FIDN model showed higher accuracy, with a mean squared error (MSE) about 82% and 67% lower than those of the predictive models based on cellular automata (CA) and the minimum travel time (MTT) approaches, respectively. Its structural similarity index measure (SSIM) averages 97%, outperforming the CA and FlamMap MTT models by 6% and 2%, respectively. Additionally, FIDN is approximately three orders of magnitude faster than both CA and MTT models. The enhanced computational efficiency and accuracy advancements offer vital insights for strategic planning and resource allocation for firefighting operations.

翻译：预测大规模野火一旦引燃后的蔓延范围，对于减少后续社会经济损失和环境破坏至关重要，但由于火行为复杂性而极具挑战。现有基于物理的模型在预测大规模或长持续时间野火事件方面存在局限。本文开发了一种基于深度学习的预测模型——Fire-Image-DenseNet (FIDN)，该模型利用从近实时数据和再分析数据中提取的环境与气象驱动因素的空间特征。我们使用2012年至2019年间美国西部发生的300多场独立野火数据对该模型进行了训练和测试。与现有模型相比，FIDN的性能不会随火灾规模或持续时间增加而下降。此外，即使在燃料密度和可燃性方面高度异质化的景观中，它也能准确预测最终烧毁面积。FIDN模型显示出更高的准确性，其均方误差（MSE）分别比基于元胞自动机（CA）和最小行进时间（MTT）方法的预测模型降低约82%和67%。其结构相似性指数（SSIM）平均达到97%，分别优于CA和FlamMap MTT模型6%和2%。此外，FIDN的计算速度比CA和MTT模型快约三个数量级。增强的计算效率和精度提升为消防作战的战略规划和资源分配提供了重要依据。