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.

翻译：大量文献致力于利用机器学习方法预测野火发生，充分利用高分辨率数据和传统基于过程的框架基本忽略的火灾预测因子。虽然机器学习分类器的标准评估指标很重要，但其对火灾危险指数预报的模型操作性能衡量可能有限。此外，模型评估时通常未能充分考虑虚警率，尽管其在操作情境中具有关键相关性。本文重新审视每日火灾危险指数模型评估范式，提出一种与真实世界决策一致的森林火灾预报模型评估新方法。我们系统评估了模型在准确预测火灾活动及减少虚警方面的性能，并进一步证明机器学习集成模型既能提升火灾识别能力，又能降低虚警率。