Research shows that some categories of human-ignited wildfires might be forecastable, due to their temporal clustering, with the possibility that resources could be pre-deployed to help reduce the incidence of such wildfires. We estimated several kinds of incendiary and other human-ignited wildfire forecast models at the weekly time step for tribal land units in the United States, evaluating their forecast skill out of sample. Analyses show that an Autoregressive Conditional Poisson (ACP) model of both incendiary and non-incendiary human-ignited wildfires is more accurate out of sample compared to alternatives, and the simplest of the ACP models performed the best. Additionally, an ensemble of these and simpler, less analytically intensive approaches performed even better. Wildfire hotspot forecast models using all model types were evaluated in a simulation mode to assess the net benefits of forecasts in the context of law enforcement resource reallocations. Our analyses show that such hotspot tools could yield large positive net benefits for the tribes in terms of suppression expenditures averted for incendiary wildfires but that the hotspot tools were less likely to be beneficial for addressing outbreaks of non-incendiary human-ignited wildfires.

中文翻译：

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人为火灾预测的净收益：以美国部落土地单位为例

研究表明，某些类别的人为引发的野火可能是可预测的，因为它们的时间聚集，并且有可能预先部署资源以帮助减少此类野火的发生率。我们在每周时间步长为美国部落土地单位估计了几种燃烧和其他人为点燃的野火预测模型，评估了他们的样本预测技能。分析表明，与替代方案相比，燃烧性和非燃烧性人燃野火的自回归条件泊松 (ACP) 模型在样本外更准确，并且最简单的 ACP 模型表现最好。此外，将这些和更简单、分析强度较低的方法结合起来表现得更好。在模拟模式下评估使用所有模型类型的野火热点预测模型，以评估在执法资源重新分配的背景下预测的净收益。我们的分析表明，就避免燃烧性野火的抑制支出而言，此类热点工具可以为部落带来巨大的积极净收益，但热点工具不太可能有助于解决非燃烧性人为点燃的野火的爆发。