发表机构
Météo-France; INRAE（法国气象局; 法国国家农业、食品与环境研究院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究提出用单调评估框架衡量野火风险系统预测分数与运行负荷关系，比较DFE、GRU模型及FARS三种方法，发现DFE单调性最佳，GRU局部强但风险水平分布不佳，FARS有局限性，好风险模型应能解释运行动态。
AI 中文摘要
使用标准机器学习指标（如F1分数或IoU）评估野火风险系统存在根本缺陷，这些指标评估的是事件预测准确性，而非连续风险信号的运行连贯性。本文提出了一种新颖的单调评估框架，用于衡量预测风险分数的增加是否与观察到的运行负荷增加一致。此外，我们在法国滨海阿尔卑斯省比较了三种结构不同的方法：基于专家的DFE指数、基于GRU的预测模型以及FARS（一种将预测性人工智能与基于大语言模型的推理相结合的混合多智能体系统）。实验结果表明，DFE尽管分类指标不佳，但在整个风险范围内表现出最平衡的单调行为。GRU模型实现了强大的局部单调性，但未能产生分布良好的风险水平。FARS继承并揭示了上游信号的结构局限性，而非纠正它们。核心发现是一个范式转变：一个好的风险模型不是准确预测火灾，而是其序数尺度能有意义地解释运行动态，本文对此进行了证明。单调框架的代码可在github上获取。
英文摘要
Evaluating wildfire risk systems using standard machine-learning metrics such as F1-score or IoU is fundamentally flawed: these metrics assess event prediction accuracy, not the operational coherence of a continuous risk signal. This work proposes a novel monotonic evaluation framework that measures whether increases in a predicted risk score consistently correspond to increases in observed operational load, such as number of fires, intervention time, and deployed resources. Moreover, we compare three structurally different approaches on the French Alpes-Maritimes department: the expert-based DFE index, GRU- based predictive models, and FARS, a hybrid multi-agent system combining predictive AI with LLM-based reasoning. Experimental results reveal that the DFE, despite poor classification metrics, exhibits the most balanced monotonic behavior across the full risk scale. GRU models achieve strong local monotonicity but fail to produce well-distributed risk levels. FARS inherits and reveals the structural limitations of upstream signals rather than correcting them. The central finding is a paradigm shift: a good risk model does not predict fires accurately, but one whose ordinal scale meaningfully explains operational dynamics, as proved in this paper. Code of the monotonic framework is available on github.
CommentsAccepted in 2026 IEEE 50th Annual Computers, Software, and Applications Conference (COMPSAC)
Journal refProc. IEEE COMPSAC 2026, pp. 764-773
DOI：10.1109/COMPSAC69091.2026.00104
