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Variance-based sensitivity analysis of model outputs using surrogate models
Shahrood University Technology.
Linköping University, Department of Computer and Information Science, Statistics. Linköping University, Faculty of Arts and Sciences.
2011 (English)In: ENVIRONMENTAL MODELLING and SOFTWARE, ISSN 1364-8152, Vol. 26, no 6, 723-730 p.Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
Elsevier Science B.V., Amsterdam. , 2011. Vol. 26, no 6, 723-730 p.
Keyword [en]
Sensitivity analysis, Surrogate models, Experimental design, Computational cost
National Category
Social Sciences
URN: urn:nbn:se:liu:diva-67311DOI: 10.1016/j.envsoft.2011.01.002ISI: 000288583600004OAI: diva2:409389
Available from: 2011-04-08 Created: 2011-04-08 Last updated: 2011-04-08

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