Robust combination of the Morris and Sobol methods in complex multidimensional models
Conducting global sensitivity analysis using variance decomposition methods in complex simulation models with many input factors is usually unaffordable. An alternative is to first apply a screening method to reduce the number of input factors and then apply a variance decomposition method to the re...
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión aceptada para publicación |
| Fecha de publicación: | 2019 |
| País: | España |
| Institución: | Basque Center for Applied Mathematics (BCAM) |
| Repositorio: | BIRD. BCAM's Institutional Repository Data |
| OAI Identifier: | oai:bird.bcamath.org:20.500.11824/1029 |
| Acceso en línea: | http://hdl.handle.net/20.500.11824/1029 |
| Access Level: | acceso embargado |
| Palabra clave: | Convergence criterion Global sensitivity analysis Morris elementary effect method Selection criterion Sobol variance decomposition method |
| Sumario: | Conducting global sensitivity analysis using variance decomposition methods in complex simulation models with many input factors is usually unaffordable. An alternative is to first apply a screening method to reduce the number of input factors and then apply a variance decomposition method to the reduced model. However, usually selection of input factors is not done robustly and convergence of the screening method is not ensured. We propose two new criteria, a criterion that mimics the visual selection of the input factors and a convergence criterion. In the application of the criteria to a complex model, the Morris screening method has needed 200 trajectories to converge and the visual criterion has outperformed other existing criteria. Our proposal ensures a robust combination of the Morris and the Sobol methods that provides an objective and automatic method to select the most important input factors with a feasible computing load to achieve convergence. |
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