Tree-based ensemble methods for sensitivity analysis of environmental models

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

Jaxa-Rozen, M. (TU Delft Policy Analysis)
Kwakkel, J.H. (TU Delft Policy Analysis)

2018

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.

Decision tree methods
Ensemble learning methods
Factor screening
Global sensitivity analysis

http://resolver.tudelft.nl/uuid:649d7279-157d-4a92-81b9-aa47b16e36cb

https://doi.org/10.1016/j.envsoft.2018.06.011

2020-06-23

1364-8152

Environmental Modelling & Software, 107, 245-266

Institutional Repository

journal article

© 2018 M. Jaxa-Rozen, J.H. Kwakkel