Complex environmental models typically require global sensitivity analysis (GSA) to account for non-linearities and parametric interactions. However, variance-based GSA is highly computationally expensive. While different screening methods can estimate GSA results, these techniques typically impose restrictions on sampling methods and input types. As an alternative, this work evaluates two decision tree-based methods to approximate GSA results: random forests, and Extra-Trees. These techniques are applicable with common sampling methods, and continuous or categorical inputs. The tree-based methods are compared to reference Sobol GSA and Morris screening techniques, for three cases: an Ishigami-Homma function, a H1N1 pandemic model, and the CDICE integrated assessment model. The Extra-Trees algorithm performs favorably compared to Morris elementary effects, accurately approximating the relative importance of Sobol total effect indices. Furthermore, Extra-Trees can estimate variable interaction importances using a pairwise permutation measure. As such, this approach could offer a user-friendly option for screening in models with inputs of mixed types.

复杂的环境模型通常需要全局敏感性分析（GSA）来考虑非线性和参数交互作用。但是，基于差异的GSA在计算上非常昂贵。尽管不同的筛选方法可以估算GSA结果，但这些技术通常会对采样方法和输入类型施加限制。作为替代方案，这项工作评估了两种基于决策树的方法来近似GSA结果：随机森林和Extra-Trees。这些技术适用于常见的采样方法以及连续或分类输入。在以下三种情况下，将基于树的方法与参考Sobol GSA和Morris筛选技术进行了比较：Ishigami-Homma函数，H1N1大流行模型和CDICE综合评估模型。与莫里斯基本效应相比，Extra-Trees算法的性能令人满意，可以准确地近似得出Sobol总效应指标的相对重要性。此外，Extra-Trees可以使用成对置换度量来估计变量交互的重要性。这样，该方法可以提供一种用户友好的选项，用于在具有混合类型输入的模型中进行筛选。