Not a fragment, but the whole: Map-based evaluation of data-driven Fire Danger Index models
This addresses the operational evaluation gap for wildfire forecasting models, though it is incremental as it focuses on improving evaluation rather than prediction itself.
The paper tackles the problem of evaluating Fire Danger Index (FDI) models for wildfire forecasting by proposing a novel evaluation method aligned with real-world decision-making, which systematically assesses fire prediction accuracy and false positives. The result shows that an ensemble of machine learning models improves fire identification and reduces false positives.
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 forecasting model that is aligned with real-world decision-making. Furthermore, we systematically assess performance in accurately predicting fire activity and the false positives (false alarms). We further demonstrate that an ensemble of ML models improves both fire identification and reduces false positives.