提出新评估方法，更贴近真实决策场景的林火预报模型效果验证。
Operational evaluation of data-driven forest fire forecasting models
- 基于真实决策需求重构林火预报模型评估范式。
- 集成学习模型在识别火灾和降低误报率上表现更优。
- 强调误报率对实际应用的关键影响，指导模型优化方向。
越来越多的研究利用机器学习方法预测野火发生，借助高分辨率数据和传统过程模型忽略的火险因子。尽管标准的机器学习分类器评价指标重要，但可能无法充分反映模型在火险指数（FDI）预报中的实际运行性能。此外，模型评估常忽视误报率的影响，而误报在实际操作中至关重要。本文重新审视每日FDI模型的评估范式，提出一种与真实决策场景一致的森林火灾预报模型评估新方法。系统评估了模型在准确预测火灾活动及误报（误报警）方面的表现。结果表明，集成机器学习模型能同时提升火灾识别能力并减少误报。
原文摘要 · Abstract (English)
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.
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