用随机森林与可解释性技术，精准预测加州林地草场火灾风险。
Probabilistic Wildfire Susceptibility from Remote Sensing Using Random Forests and SHAP
- 结合随机森林与SHAP分析，量化不同生态区火灾驱动因素。
- 林地火灾预测AUC达0.997，时序验证森林泛化能力提升至0.6615。
- 揭示土壤碳、树冠覆盖等关键因子，适合应急规划与气候应对者参考。
野火对全球生态系统构成重大威胁，加州因气候、地形、植被及人类活动等因素频发火灾。本研究基于随机森林（RF）算法，融合可解释人工智能（XAI）中的Shapley Additive exPlanations（SHAP），构建加州全面的火灾风险图。模型性能通过空间与时间双重验证评估：在草地与林地的判别能力接近完美（AUC分别为0.996和0.997）。空间交叉验证显示中等可迁移性（林地ROC-AUC=0.6155，草地=0.5416），而时序分割验证表明更强泛化能力，尤其林地表现优异（ROC-AUC=0.6615，PR-AUC=0.8423）。SHAP分析识别出关键驱动因子：林地为土壤有机碳、树冠覆盖与归一化植被指数（NDVI）；草地则以地表温度（LST）、高程及植被健康指数为主。区域分类显示，中央谷地与北布特县草地高风险集中，北布特与北海岸红木区林地高风险显著。该RF-SHAP框架提供可解释、稳健且可适配的风险评估方法，支持科学决策与精准减灾策略制定。
原文摘要 · Abstract (English)
Wildfires pose a significant global threat to ecosystems worldwide, with California experiencing recurring fires due to various factors, including climate, topographical features, vegetation patterns, and human activities. This study aims to develop a comprehensive wildfire risk map for California by applying the random forest (RF) algorithm, augmented with Explainable Artificial Intelligence (XAI) through Shapley Additive exPlanations (SHAP), to interpret model predictions. Model performance was assessed using both spatial and temporal validation strategies. The RF model demonstrated strong predictive performance, achieving near-perfect discrimination for grasslands (AUC = 0.996) and forests (AUC = 0.997). Spatial cross-validation revealed moderate transferability, yielding ROC-AUC values of 0.6155 for forests and 0.5416 for grasslands. In contrast, temporal split validation showed enhanced generalization, especially for forests (ROC-AUC = 0.6615, PR-AUC = 0.8423). SHAP-based XAI analysis identified key ecosystem-specific drivers: soil organic carbon, tree cover, and Normalized Difference Vegetation Index (NDVI) emerged as the most influential in forests, whereas Land Surface Temperature (LST), elevation, and vegetation health indices were dominant in grasslands. District-level classification revealed that Central Valley and Northern Buttes districts had the highest concentration of high-risk grasslands, while Northern Buttes and North Coast Redwoods dominated forested high-risk areas. This RF-SHAP framework offers a robust, comprehensible, and adaptable method for assessing wildfire risks, enabling informed decisions and creating targeted strategies to mitigate dangers.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。