Tree-based ensemble methods for sensitivity analysis of environmental models: A performance comparison with Sobol and Morris techniques

Marc Jaxa-Rozen, Jan Kwakkel

Research output: Contribution to journalArticleScientificpeer-review

20 Citations (Scopus)
13 Downloads (Pure)

Abstract

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.

Original languageEnglish
Pages (from-to)245-266
Number of pages22
JournalEnvironmental Modelling and Software
Volume107
DOIs
Publication statusPublished - 2018

Keywords

  • Decision tree methods
  • Ensemble learning methods
  • Factor screening
  • Global sensitivity analysis

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