AI RESEARCH
Not a fragment, but the whole: Map-based evaluation of data-driven Fire Danger Index models
arXiv CS.LG
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ArXi:2603.25469v1 Announce Type: new A growing body of literature has focused on predicting wildfire occurrence using machine learning methods, capitalizing on high-resolution data and fire predictors that canonical process-based frameworks largely ignore. Standard evaluation metrics for an ML classifier, while important, provide a potentially limited measure of the model's operational performance for the Fire Danger Index (FDI) forecast.