Wildfires are highly imbalanced natural hazards in both space and severity, making the prediction of extreme events particularly challenging. In this work, we introduce the first ordinal classification framework for forecasting wildfire severity levels directly aligned with operational decision-making in France. Our study investigates the influence of loss-function design on the ability of neural models to predict rare yet critical high-severity fire occurrences. We compare standard cross-entropy with several ordinal-aware objectives, including the proposed probabilistic TDeGPD loss derived from a truncated discrete exponentiated Generalized Pareto Distribution. Through extensive benchmarking over multiple architectures and real operational data, we show that ordinal supervision substantially improves model performance over conventional approaches. In particular, the Weighted Kappa Loss (WKLoss) achieves the best overall results, with more than +0.1 IoU (Intersection Over Union) gain on the most extreme severity classes while maintaining competitive calibration quality. However, performance remains limited for the rarest events due to their extremely low representation in the dataset. These findings highlight the importance of integrating both severity ordering, data imbalance considerations, and seasonality risk into wildfire forecasting systems. Future work will focus on incorporating seasonal dynamics and uncertainty information into training to further improve the reliability of extreme-event prediction.

翻译：野火在空间分布和严重程度上均呈现高度不平衡的自然灾害特征，使得极端事件的预测尤为困难。本研究首次提出一种序数分类框架，用于预测与法国实际决策直接对应的野火严重程度等级。我们探究了损失函数设计对神经网络模型预测罕见但关键的高严重度火灾事件能力的影响。我们比较了标准交叉熵损失与多种考虑序数关系的目标函数，包括基于截断离散指数化广义帕累托分布提出的概率型TDeGPD损失。通过对多种架构和实际业务数据进行广泛基准测试，我们发现序数监督显著提升了模型性能，优于传统方法。其中，加权Kappa损失（WKLoss）取得了最佳综合效果，在最具极端性的严重等级上获得了超过+0.1的交并比增益，同时保持了具有竞争力的校准质量。然而，由于数据集中极低代表性，对最罕见事件的预测性能仍存在局限。这些发现凸显了将严重程度排序、数据不平衡性考量以及季节性风险整合到野火预测系统中的重要性。未来工作将聚焦于将季节动态性和不确定性信息纳入训练过程，以进一步提升极端事件预测的可靠性。