Understanding the complex interrelationships between wildfire and its environmental and anthropogenic controls is crucial for wildfire modeling and management. Although machine learning (ML) models have yielded significant improvements in wildfire predictions, their limited interpretability has been an obstacle for their use in advancing understanding of wildfires. This study builds an ML model incorporating predictors of local meteorology, land-surface characteristics, and socioeconomic variables to predict monthly burned area at grid cells of 0.25° × 0.25° resolution over the contiguous United States. Besides these predictors, we construct and include predictors representing the large-scale circulation patterns conducive to wildfires, which largely improves the temporal correlations in several regions by 14%–44%. The Shapley additive explanation is introduced to quantify the contributions of the predictors to burned area. Results show a key role of longitude and latitude in delineating fire regimes with different temporal patterns of burned area. The model captures the physical relationship between burned area and vapor pressure deficit, relative humidity (RH), and energy release component (ERC), in agreement with the prior findings. Aggregating the contribution of predictor variables of all the grids by region, analyses show that ERC is the major contributor accounting for 14%–27% to large burned areas in the western US. In contrast, there is no leading factor contributing to large burned areas in the eastern US, although large-scale circulation patterns featuring less active upper-level ridge-trough and low RH two months earlier in winter contribute relatively more to large burned areas in spring in the southeastern US.

中文翻译：

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使用机器学习和博弈论解释确定美国本土野火的关键驱动因素


了解野火与其环境和人为控制之间复杂的相互关系对于野火建模和管理至关重要。尽管机器学习（ML）模型在野火预测方面取得了显着进步，但其有限的可解释性一直是其用于增进对野火的理解的障碍。本研究构建了一个结合当地气象、地表特征和社会经济变量的预测因子的 ML 模型，以预测美国本土 0.25° × 0.25° 分辨率的网格单元的每月燃烧面积。除了这些预测变量之外，我们还构建并包含了代表有利于野火的大规模环流模式的预测变量，这在很大程度上将几个地区的时间相关性提高了 14%–44%。引入 Shapley 附加解释来量化预测变量对烧伤面积的贡献。结果表明，经度和纬度在描绘不同时间模式的烧毁区域的火势方面发挥着关键作用。该模型捕捉了燃烧面积与蒸气压不足、相对湿度 (RH) 和能量释放成分 (ERC) 之间的物理关系，与之前的发现一致。按地区汇总所有电网预测变量的贡献，分析表明，ERC 是主要贡献者，占美国西部大面积烧毁面积的 14%–27%。相比之下，尽管冬季提前两个月上层脊槽活动较少且相对湿度较低的大范围环流模式对春季大面积烧毁面积的贡献相对较大，但美国东部并没有导致大面积烧毁的主导因素。在美国东南部。