aiqbench analysis
This research challenges standard classification metrics like F1-score for wildfire risk systems, arguing instead that operational coherence—where higher predicted scores consistently correlate with increased resource deployment—is the true measure of model utility. By comparing expert indices, GRU-based models, and a hybrid AI-LLM system in France's Alpes-Maritimes region, the study demonstrates how predictive accuracy alone can be misleading without monotonic behavior across risk scales. The proposed framework offers a critical new lens for evaluating continuous risk signals where operational dynamics matter more than binary event prediction.
Risk Is Not the Target: A Monotonic Framework for Evaluating Wildfire Operational Risk Signals
arXiv:2607.21597v1 Announce Type: new Abstract: 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.
Abstract: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.
Artificial Intelligence (cs.AI)
arXiv:2607.21597 [cs.AI]
(or arXiv:2607.21597v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2607.21597 Focus to learn more arXiv-issued DOI via DataCite
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