使用空间 Super Learner 方法对加利福尼亚州荒野火灾燃烧严重程度进行建模
Modelling wildland fire burn severity in California using a spatial Super Learner approach
AI总结：
本研究开发了一种基于空间Super Learner算法的机器学习模型，利用火前遥感数据预测加州荒野火灾的燃烧严重程度，并发现其优于标准线性回归，为社区制定早期火灾探测和资源分配等干预策略提供了可操作的见解。
摘要：
鉴于美国西部荒野火灾日益频发，开发能够理解和准确预测燃烧严重程度的工具存在迫切需求。我们开发了一种机器学习模型，利用火前遥感数据预测火后燃烧严重程度。从加利福尼亚州四个地区——Kincade火灾（2019年）、CZU Lightning Complex火灾（2020年）、Windy火灾（2021年）和KNP火灾（2021年）的发生地——收集的水文、生态和地形变量，被用作差分归一化燃烧比的预测因子。我们假设一种利用Vecchia高斯近似来考虑空间自相关的Super Learner（SL）算法，能够准确对燃烧严重程度进行建模。在所探索的所有测试集和训练集组合中，我们模型的结果显示SL算法优于标准线性回归方法。在拟合并验证SL模型性能后，我们使用可解释的机器学习工具来确定严重燃烧破坏的主要驱动因素，包括绿度、海拔和火灾天气变量。这些发现提供了可操作的见解，使社区能够制定干预策略，例如早期火灾探测系统、火灾季前植被清理活动以及紧急响应期间的资源分配。实施后，该模型有望最大限度地减少加利福尼亚州的人员生命、财产、资源和生态系统的损失。
英文摘要：
Given the increasing prevalence of wildland fires in the Western US, there is a critical need to develop tools to understand and accurately predict burn severity. We develop a machine learning model to predict post-fire burn severity using pre-fire remotely sensed data. Hydrological, ecological, and topographical variables collected from four regions of California - the sites of the Kincade fire (2019), the CZU Lightning Complex fire (2020), the Windy fire (2021), and the KNP Fire (2021) - are used as predictors of the difference normalized burn ratio. We hypothesize that a Super Learner (SL) algorithm that accounts for spatial autocorrelation using Vecchia's Gaussian approximation will accurately model burn severity. In all combinations of test and training sets explored, the results of our model showed the SL algorithm outperformed standard Linear Regression methods. After fitting and verifying the performance of the SL model, we use interpretable machine learning tools to determine the main drivers of severe burn damage, including greenness, elevation and fire weather variables. These findings provide actionable insights that enable communities to strategize interventions, such as early fire detection systems, pre-fire season vegetation clearing activities, and resource allocation during emergency responses. When implemented, this model has the potential to minimize the loss of human life, property, resources, and ecosystems in California.
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