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Abstract
Variance-based approaches are widely used for Global Sensitivity Analysis (GSA) of environmental models. However, methods that consider the entire Probability Density Function (PDF) of the model output, rather than its variance only, are preferable in cases where variance is not an adequate proxy of uncertainty, e.g. when the output distribution is highly-skewed or when it is multi-modal. Still, the adoption of density-based methods has been limited so far, possibly because they are relatively more difficult to implement. Here we present a novel GSA method, called PAWN, to efficiently compute density-based sensitivity indices. The key idea is to characterise output distributions by their Cumulative Distribution Functions (CDF), which are easier to derive than PDFs. We discuss and demonstrate the advantages of PAWN through applications to numerical and environmental modelling examples. We expect PAWN to increase the application of density-based approaches and to be a complementary approach to variance-based GSA.
Original language | English |
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Pages (from-to) | 1-11 |
Number of pages | 11 |
Journal | Environmental Modelling and Software |
Volume | 67 |
DOIs | |
Publication status | Published - 1 May 2015 |
Keywords
- Density-based sensitivity indices
- Global sensitivity analysis
- Uncertainty analysis
- Variance-based sensitivity indices
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Dive into the research topics of 'A simple and efficient method for global sensitivity analysis based on cumulative distribution functions'. Together they form a unique fingerprint.Projects
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Profiles
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Dr Francesca Pianosi
- Department of Civil Engineering - Senior Lecturer in Water and Environmental Engineering
- Water and Environmental Engineering
- Cabot Institute for the Environment
Person: Academic , Member