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Development of a surrogate model and sensitivity analysis for spatio-temporal numerical simulators

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Abstract

To evaluate the consequences on human health of radionuclide releases in the environment, numerical simulators are used to model the radionuclide atmospheric dispersion. These codes can be time consuming and depend on many uncertain variables related to radionuclide, release or weather conditions. These variables are of different kind: scalar, functional and qualitative. Given the uncertain parameters, code provides spatial maps of radionuclide concentration for various moments. The objective is to assess how these uncertainties can affect the code predictions and to perform a global sensitivity analysis of code in order to identify the most influential uncertain parameters.

This sensitivity analysis often calls for the estimation of variance-based importance measures, called Sobol’ indices. To estimate these indices, we propose a global methodology combining several advanced statistical techniques which enable to deal with the various natures of the uncertain inputs and the high dimension of model outputs. First, a quantification of input uncertainties is made based on data analysis and expert judgment. Then, an initial realistic sampling design is generated and the corresponding code simulations are performed. Based on this sample, a proper orthogonal decomposition of the spatial output is used and the main decomposition coefficients are modeled with Gaussian process surrogate model. The obtained spatial metamodel is then used to compute spatial maps of Sobol’ indices, yielding the identification of global and local influence of each input variable and the detection of areas with interactions. The impact of uncertainty quantification step on the results is also evaluated.

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References

  • Akaike H (1974) A new look at the statistical model identification. IEEE Trans Autom Control 19:716–723

    Article  Google Scholar 

  • Bayarri M, Berger J, Cafeo J, Garcia-Donato G, Liu F, Palomo J, Parthasarathy R, Paulo R, Sacks J, Walsh D (2007) Computer model validation with functional output. Ann Stat 35:1874–1906

    Article  Google Scholar 

  • Campbell K, McKay M, Williams B (2006) Sensitivity analysis when model outputs are functions. Reliab Eng Sys Saf 91:1468–1472

    Article  Google Scholar 

  • Chatterjee A (2000) An introduction to the proper orthogonal decomposition. Curr Sci 78:808–817

    Google Scholar 

  • Chilès J-P, Delfiner P (1999) Geostatistics: modeling spatial uncertainty. Wiley, New-York

    Book  Google Scholar 

  • Ciriello V, Di Federico V, Riva M, Cadini F, De Sanctis J, Zio E, Guadagnini A (2012) Polynomial chaos expansion for global sensitivity analysis applied to a model of radionuclide migration in a randomly heterogeneous aquifer. Stoch Env Res Risk Assess 27:945–954

    Article  Google Scholar 

  • De Rocquigny E (2012) Modelling under risk and uncertainty: an introduction to statistical, phenomenological and computational methods. Wiley, New York, NY

    Book  Google Scholar 

  • De Rocquigny E, Devictor N, Tarantola S (2008) Uncertainty in Industrial Practice. Wiley, New York, NY

    Book  Google Scholar 

  • Doury A (1980) Pratiques françaises en matière de dispersion quantitative de la pollution atmosphérique potentielle liée aux activités nucléaires. Séminaire sur les rejets radioactifs et leur dispersion dans l’atmosphère à la suite d’un accident hypothétique de réacteur. RISQ, Danemark

    Google Scholar 

  • Efron B, Stein C (1981) The jacknife estimate of variance. Ann Stat 9:586–596

    Article  Google Scholar 

  • Fang K-T, Li R, Sudjianto A (2006) Design and modeling for computer experiments. Chapman & Hall/CRC

  • Fasso A, Esposito A, Porcu E, Reverberi AP, Veglio F (2003) Statistical sensitivity analysis of packed column reactors for contaminated wastewater. Environmetrics 14:743–759

    Article  CAS  Google Scholar 

  • Hastie T, Tibshirani R (1990) Generalized additive models. Chapman & Hall/CRC, New York, NY, USA

    Google Scholar 

  • Helton J (1993) Uncertainty and sensitivity analysis techniques for use in performance assessment for radioactive waste disposal. Reliab Sys Saf 42:327–367

    Article  Google Scholar 

  • Higdon D, Gattiker J, Williams B, Rightley M (2008) Computer model calibration using high-dimensional output. J Am Stat Assoc 103:571–583

    Article  Google Scholar 

  • Homma T, Saltelli A (1996) Importance measures in global sensitivity analysis of non linear models. Reliab Eng Sys Saf 52:1–17

    Article  Google Scholar 

  • Hyndman R, Shang H-L (2010) Rainbow plots, bagplots and boxplots for functional data. J Comput Graph Stat 19(1):29–45

    Article  Google Scholar 

  • Johnson ME, Moore LM, Ylvisaker D (1990) Minimax and maximin distance designs. J Stal Plan Inference 26:131–148

    Article  Google Scholar 

  • Lamboni M, Monod H, Makowski D (2011) Multivariate sensitivity analysis to measure global contribution of input factors in dynamic models. Reliab Eng Sys Saf 96:450–459

    Article  Google Scholar 

  • Lilburne L, Tarantola S (2009) Sensitivity analysis of spatial models. Int J Geogr Inf Sci 23:151–168

    Article  Google Scholar 

  • Marrel A, Iooss B, Van Dorpe F, Volkova E (2008) An efficient methodology for modeling complex computer codes with Gaussian processes. Comput Stat Data Anal 52:4731–4744

    Article  Google Scholar 

  • Marrel A, Iooss B, Jullien M, Laurent B, Volkova E (2011) Global sensitivity analysis for models with spatially dependent outputs. Environmetrics 22:383–397

    Article  Google Scholar 

  • McKay MD, Conover WJ, Beckman RJ (1979) A comparison of three methods for selecting values of input variables in the analysis of output from a computer code. Technometrics 21:239–245

    Google Scholar 

  • Monfort M, Patryl L, Armand P (2011). Presentation of the CERES platform used to evaluate the consequences of the emissions of pollutants in the environment. International conference on radioecology & environmental radioactivity: environment & nuclear renaissance

  • Nychka D, Cox L, Piegorsch W (1998) Case Studies in Environmental Statistics. Springer Verlag, Berlin

    Book  Google Scholar 

  • Oakley J, O’Hagan A (2002) Bayesian inference for the uncertainty distribution. Biometrika 89:769–784

    Article  Google Scholar 

  • Rasmussen CE, Williams C (2006) Gaussian Processes for Machine Learning. The MIT Press, Cambridge

    Google Scholar 

  • Rousseeuw PJ, Ruts I, Tukey JW (1999) The bagplot: a bivariate boxplot. Am Stat 53:382–387

    Google Scholar 

  • Sacks J, Welch WJ, Mitchell TJ, Wynn HP (1989) Design and analysis of computer experiments. Stat Sci 4:409–435

    Article  Google Scholar 

  • Saltelli A, Chan K, Scott EM (eds) (2000) Sensitivity analysis. Wiley series in probability and statistics. Wiley, New York, NY, USA

    Google Scholar 

  • Saltelli A, Annoni P, Azzini I, Campolongo F, Ratto M, Tarantola S (2010) Variance based sensitivity analysis of model output. Design and estimator for the total sensitivity index. Comput Phys Commun 181:259–270

    Article  CAS  Google Scholar 

  • Shi J, Wang B, Murray-Smith R, Titterington D (2007) Gaussian process functional regression modeling for batch data. Biometrics 63:714–723

    Article  CAS  Google Scholar 

  • Sobol IM (1993) Sensitivity estimates for non linear mathematical models. Math Model Comput Exp 1:407–414

    Google Scholar 

  • Sobol IM (2001) Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates. Math Comput Simul 5:271–280

    Article  Google Scholar 

  • Tukey JW (1975) Mathematics and the picturing of data. In: Proceedings of the international congress of mathematicians, vol 2, p. 523–521

  • Volkova E, Iooss B, Van Dorpe F (2008) Global sensitivity analysis for a numerical model of radionuclide migration from the RRC “Kurchatov Institute” radwaste disposal site. Stoch Environ Res Risk Asses 22:17–31

    Article  Google Scholar 

  • Welch W, Buck R, Sacks J, Wynn H, Mitchell T, Morris M (1992) Screening, predicting, and computer experiments. Technometrics 34:15–25

    Article  Google Scholar 

Download references

Acknowledgments

This work was supported by French National Research Agency (ANR) through COSINUS program (project COSTA BRAVA no ANR-09-COSI-015).

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Correspondence to Amandine Marrel.

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Marrel, A., Perot, N. & Mottet, C. Development of a surrogate model and sensitivity analysis for spatio-temporal numerical simulators. Stoch Environ Res Risk Assess 29, 959–974 (2015). https://doi.org/10.1007/s00477-014-0927-y

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  • DOI: https://doi.org/10.1007/s00477-014-0927-y

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