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Latin hypercube sampling of Gaussian random field for Sobol' global sensitivity analysis of models with spatial inputs and scalar output

Abstract : The variance-based Sobol' approach is one of the few global sensitivity analysis techniques that is suitable for complex models with spatially distributed inputs. Yet it needs a large number of model runs to compute sensitivity indices: in the case of models where some inputs are 2D Gaussian random fields, it is of great importance to generate a relatively small set of map realizations capturing most of the variability of the spatial inputs. The purpose of this paper is to discuss the use of Latin Hypercube Sampling (LHS) of geostatistical simulations to reach better efficiency in the computation of Sobol' sensitivity indices on spatial models. Sensitivity indices are estimated on a simple analytical model with a spatial input, for increasing sample size, using either Simple Random Sampling (SRS) or LHS to generate input map realizations. Results show that using LHS rather than SRS yields sensitivity indices estimates which are slightly more precise (smaller variance), with no significant improvement of bias.
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https://hal.inrae.fr/hal-02593387
Contributor : Migration Irstea Publications <>
Submitted on : Friday, May 15, 2020 - 5:14:36 PM
Last modification on : Friday, April 16, 2021 - 3:29:24 AM

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  • HAL Id : hal-02593387, version 1
  • IRSTEA : PUB00029065

Citation

Nathalie Saint Geours, Jean-Stéphane Bailly, F. Grelot, C. Lavergne. Latin hypercube sampling of Gaussian random field for Sobol' global sensitivity analysis of models with spatial inputs and scalar output. Accuracy2010, Jul 2010, Leicester, United Kingdom. pp.81-85. ⟨hal-02593387⟩

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