Operational evaluation of data-driven forest fire forecasting models
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. Furthermore, model evaluation is frequently conducted without adequately accounting for false positive rates, despite their critical relevance in operational contexts. In this paper, we revisit the daily FDI model evaluation paradigm and propose a novel method for evaluating a forest fire f...
| Authors | Shahbaz Alvi, Italo Epicoco, Jose Maria Costa Saura |
|---|---|
| Primary category | cs.LG |
| Published | 26 March 2026 |
| Last updated at source | 28 July 2026 |
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