Research that illuminates causes of urban forest storm damage is valuable for planning and management. However, logistical and safety concerns often delay post-storm surveys in urban areas; thus, surveys may include observations with unverified sources of damage. While this uncertainty is often ignored, it can make up a high proportion of the number of damaged trees. The goal of this research was to improve understanding of techniques for modeling storm damage in urban forests. Using urban forest storm damage inventories collected in Florida, post-Hurricane Irma (2017), we tested how different imputation methods, modeling procedures, and damage frequency levels could impact overall model results. We utilized machine learning algorithms Random Forest (RF) and k-Nearest Neighbors (KNN), and generalized linear models (GLM). We found that GLM and RF models gave overall unbiased predictions of damage across all methods and rarity levels, while KNN consistently under-predicted damage. Damage frequency influenced some measures of performance but did not impact variable significance. Imputation methods all identified consistent variables of most significance within each model procedure; however, there was variation among variables ranked moderately important. While both GLM and RF models identified plot tree basal area as highly significant damage predictors, they otherwise disagreed on individual variable importance. These findings suggest that while explanatory models for urban forest storm damage can be achieved by examining linear relationships, more complex relationships such as the ones identified by RF models can have equal explanatory power with different subsets of predictor variables.

阐明城市森林风暴破坏原因的研究对于规划和管理很有价值。然而，后勤和安全问题往往会延迟城市地区的风暴后调查；因此，调查可能包括对未经证实的损害来源的观察。虽然这种不确定性经常被忽略，但它可以占受损树木数量的很大一部分。这项研究的目的是提高对城市森林风暴破坏建模技术的理解。使用在飓风艾尔玛 (2017 年) 后佛罗里达州收集的城市森林风暴破坏清单，我们测试了不同的估算方法、建模程序和破坏频率水平如何影响整体模型结果。我们利用机器学习算法随机森林 (RF) 和 k 近邻 (KNN)，以及广义线性模型 (GLM)。我们发现 GLM 和 RF 模型对所有方法和稀有度级别的损坏做出了总体无偏的预测，而 KNN 始终低估了损坏的预测。损坏频率影响了一些性能指标，但不影响变量的重要性。插补方法都确定了每个模型程序中最重要的一致变量；然而，排名中等重要的变量之间存在差异。虽然 GLM 和 RF 模型都将地块树基础区域确定为非常重要的损伤预测因子，但它们在个别变量的重要性上存在分歧。这些发现表明，虽然可以通过检查线性关系来获得城市森林风暴破坏的解释模型，