Extreme-value forest fire prediction A study of the Loss Function in an Ordinality Scheme

arXiv:2601.03327v3 Announce Type: replace Abstract: 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, includi

Source: arXiv cs.LG — read the full report at the original publisher.

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