Variance-based sensitivity analysis of model outputs using surrogate models

If a computer model is run many times with different inputs, the results obtained can often be used to derive a computationally cheaper approximation, or surrogate model, of the original computer code. Thereafter, the surrogate model can be employed to reduce the computational cost of a variance-based sensitivity analysis (VBSA) of the model output. Here, we draw attention to a procedure in which an adaptive sequential design is employed to derive surrogate models and estimate sensitivity indices for different sub-groups of inputs. The results of such group-wise VBSAs are then used to select inputs for a final VBSA. Our procedure is particularly useful when there is little prior knowledge about the response surface and the aim is to explore both the global variability and local nonlinear features of the model output. Our conclusions are based on computer experiments involving the process-based river basin model INCA-N, in which outputs like the average annual riverine load of nitrogen can be regarded as functions of 19 model parameters.

Research highlights
► Our developed sequential design and prediction technique could construct a surrogate model.
► Surrogate models reduce the computational cost of sensitivity analysis of complex deterministic models.
► Grouping of the inputs and derivation of surrogate model for each can be done in sensitivity analysis.
► Sensitivity analysis by our technique provides information about nonlinear response to the output.
► Surrogate models offer a basis for interactive decision support tools and learning processes.