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Article dans une revue

Dynamic sensitivity analysis of long-running landslide models through basis set expansion and meta-modelling

Abstract : Predicting the temporal evolution of landslides is typically supported by numerical modelling. Dynamic sensitivity analysis aims at assessing the influence of the landslide properties on the time-dependent predictions (e.g. time series of landslide displacements). Yet, two major difficulties arise: (1) Global sensitivity analysis require running the landslide model a high number of times (>1,000), which may become impracticable when the landslide model has a high computation time cost (>several hours); (2) Landslide model outputs are not scalar, but function of time, that is, they are n-dimensional vectors with n usually ranging from 100 to 1,000. In this article, I explore the use of a basis set expansion, such as principal component analysis, to reduce the output dimensionality to a few components, each of them being interpreted as a dominant mode of variation in the overall structure of the temporal evolution. The computationally intensive calculation of the Sobol' indices for each of these components are then achieved through meta-modelling, that is, by replacing the landslide model by a "costless-to-evaluate" approximation (e.g. a projection pursuit regression model). The methodology combining "basis set expansion--meta-model--Sobol' indices" is then applied to the Swiss La Frasse landslide to investigate the dynamic sensitivity analysis of the surface horizontal displacements to the slip surface properties during the pore pressure changes. I show how to extract information on the sensitivity of each main modes of temporal behaviour using a limited number (a few tens) of long-running simulations.
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Contributeur : Jérémy Rohmer <>
Soumis le : dimanche 25 août 2013 - 11:06:59
Dernière modification le : mardi 31 mars 2020 - 10:46:09




Jeremy Rohmer. Dynamic sensitivity analysis of long-running landslide models through basis set expansion and meta-modelling. Natural Hazards, Springer Verlag, 2014, 73 (1), pp.5-22. ⟨10.1007/s11069-012-0536-3⟩. ⟨hal-00853933⟩



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