Sensitivity analysis practices: Strategies for model-based inference

By Andrea Saltelli, Marco Ratto, Stefano Tarantola, Francesca Campolongo. Reliability Engineering & System Safety, 2006

618 citations recorded by the source corpus.

Subfields: Genetics, Materials Chemistry, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/74a7b3902b7cb5c72948fd65ae5376823d9aa413

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Citations in the corpus

A fully multiple-criteria implementation of the Sobol' method for parameter sensitivity analysis (2012, methodology): “[7] Sobol′ analysis estimates, via approximate Monte Carlo integrations, what fraction of the variability of some entity can be attributed to changes in the values of various factors that control the value of that entity [Sobol′, 1993; Saltelli et al., 1999, 2006; Hall et al., 2005].”
Parameter uncertainty analysis for an operational hydrological model using residual-based and limits of acceptability approaches (2018, methodology): “An increased awareness of these modeling uncertainties and the need for quality control of such models requires the integration of uncertainty analysis into the modeling process from the very beginning (Beven, 1989; Saltelli et al., 2006; Refsgaard et al., 2007).”
Sobol' sensitivity analysis of a complex environmental model (2011, background): “To enhance the extraction of such information, another objective is to calculate the second order sensitivity indices for the most important parameters, in order to estimate the most significant interactions between pairs of parameters (Saltelli et al., 2006).”
Efficient posterior exploration of a high‐dimensional groundwater model from two‐stage Markov chain Monte Carlo simulation and polynomial chaos expansion (2013, methodology): “[69] The gPC technique is well suited for global sensitivity analysis (GSA) [e.g., Saltelli et al., 2006] because the associated computational cost becomes practically equivalent to the time it takes to estimate the expansion coefficients [e.g., Sudret, 2008].”
Towards simplification of hydrologic modeling: identification of dominant processes (2016, methodology): “The Fourier amplitude sensitivity test (FAST) (Schaibly and Shuler, 1973; Cukier et al., 1973, 1975; Saltelli et al., 2006) was selected for this study because it has been demonstrated that it can efficiently estimate non-linear hydrologic model parameter sensitivity (Guse et al., 2014; Pfannerstill”
Probabilistic assessment of seawater intrusion under multiple sources of uncertainty (2015, background): “Sobol indices provide generally robust results, as these are not constrained by any linearity assumption on the underlying mathematical model [34], a feature which is particularly critical in a complex system of the kind we investigate.”
Quantifying parameter sensitivity, interaction, and transferability in hydrologically enhanced versions of the Noah land surface model over transition zones during the warm season (2010, methodology): “…identifies critical regions in the space of the inputs, unravels parameter interactions, establishes priorities for research, and, through an interactive process of revising the model structure, leads to simplified models and increased understanding of the natural system [Saltelli et al., 2006].”
An effective screening design for sensitivity analysis of large models (2007, methodology): “It is confirmed that the EE method is a sound practice and always to be preferred to the many local analyses still found in the literature (see, for instance, Murphy et al., 2004; or Saltelli et al., 2006, for a review).”