On the Relative Importance of Input Factors in Mathematical Models Safety Assessment for Nuclear Waste Disposal

• Published in: Journal of the American Statistical Association Vol. 97; no. 459; pp. 702 - 709
• Main Authors: Saltelli, Andrea, Tarantola, Stefano
• Format: Journal Article
• Language: English
• Published: Alexandria, VA Taylor & Francis 01.09.2002; American Statistical Association; Taylor & Francis Ltd
• Subjects: Analysis of variance; Applications and Case Studies; Correlated input; Cost estimation models; Distribution theory; Exact sciences and technology; Geosphere; Linear inference, regression; Lotteries; Mathematical models; Mathematics; Modeling; Modelling; Nonadditive model; Nuclear accidents & safety; Nuclear energy; Nuclear waste disposal; Probability and statistics; Radioactive waste; Radioactive wastes; Radionuclides; Risk analysis; Sciences and techniques of general use; Sensitivity analysis; Setting; Statistical methods; Statistical variance; Statistics; Waste disposal; Waste management; Nuclear industry; Monte Carlo method; Correlation; Sensitivity analysis; Statistical distribution; Covariance analysis; Computation code; Variance analysis; Waste disposal; Additive model; Variance; Nuclear waste disposal; Statistical regression; Correlation analysis; Variance reduction; Nonadditive model; Approximation theory; Safety; Mathematical model; Computing method; Quantitative analysis
• ISSN: 0162-1459; 1537-274X
• DOI: 10.1198/016214502388618447

This article deals with global quantitative sensitivity analysis of the Level E model, a computer code used in safety assessment for nuclear waste disposal. The Level E code has been the subject of two international benchmarks of risk assessment codes and Monte Carlo methods and is well known in the literature. We discuss the Level E model with reference to two different settings. In the first setting, the objective is to find the input factor that drives most of the output variance. In the second setting, we strive to achieve a preestablished reduction in the variance of the model output by fixing the smallest number of factors. The emphasis of this work is on how to define the concept of importance in an unambiguous way and how to assess it in the simultaneous occurrence of correlated input factors and non-additive models.