Wildfires are among the most severe natural hazards, posing a significant threat to both humans and natural ecosystems. The growing risk of wildfires increases the demand for forecasting models that are not only accurate but also reliable. Deep Learning (DL) has shown promise in predicting wildfire danger; however, its adoption is hindered by concerns over the reliability of its predictions, some of which stem from the lack of uncertainty quantification. To address this challenge, we present an uncertainty-aware DL framework that jointly captures epistemic (model) and aleatoric (data) uncertainty to enhance short-term wildfire danger forecasting. In the next-day forecasting, our best-performing model improves the F1 Score by 2.3% and reduces the Expected Calibration Error by 2.1% compared to a deterministic baseline, enhancing both predictive skill and calibration. Our experiments confirm the reliability of the uncertainty estimates and illustrate their practical utility for decision support, including the identification of uncertainty thresholds for rejecting low-confidence predictions and the generation of well-calibrated wildfire danger maps with accompanying uncertainty layers. Extending the forecast horizon up to ten days, we observe that aleatoric uncertainty increases with time, showing greater variability in environmental conditions, while epistemic uncertainty remains stable. Finally, we show that although the two uncertainty types may be redundant in low-uncertainty cases, they provide complementary insights under more challenging conditions, underscoring the value of their joint modeling for robust wildfire danger prediction. In summary, our approach significantly improves the accuracy and reliability of wildfire danger forecasting, advancing the development of trustworthy wildfire DL systems.

翻译：野火是最严重的自然灾害之一，对人类和自然生态系统构成重大威胁。日益增加的野火风险使得对既准确又可靠的预报模型的需求愈发迫切。深度学习在预测野火危险性方面展现出潜力，然而，其应用因对其预测可靠性的担忧而受到阻碍，部分原因在于缺乏不确定性量化。为应对这一挑战，我们提出了一种考虑不确定性的深度学习框架，该框架联合捕获认知（模型）不确定性与偶然（数据）不确定性，以增强短期野火危险性预报能力。在次日预报任务中，与确定性基线模型相比，我们的最佳模型将F1分数提升了2.3%，并将期望校准误差降低了2.1%，从而同时提高了预测性能与校准质量。实验证实了不确定性估计的可靠性，并展示了其在决策支持中的实用价值，包括识别用于拒绝低置信度预测的不确定性阈值，以及生成带有相应不确定性层的良好校准的野火危险地图。将预报时限延长至十天，我们观察到偶然不确定性随时间增加，反映出环境条件的更大变异性，而认知不确定性保持稳定。最后，我们表明，尽管在低不确定性情况下两种不确定性类型可能冗余，但在更具挑战性的条件下它们能提供互补性见解，凸显了联合建模对于稳健野火危险性预测的价值。总之，我们的方法显著提高了野火危险性预报的准确性和可靠性，推动了可信赖的野火深度学习系统的发展。