hal-00853933
https://brgm.hal.science/hal-00853933
doi:10.1007/s11069-012-0536-3
[BRGM] Bureau de Recherches Géologiques et Minières
[TDS-MACS] Réseau de recherche en Théorie des Systèmes Distribués, Modélisation, Analyse et Contrôle des Systèmes
[BRGM-DO] BRGM - Publications des Directions Opérationnelles
Dynamic sensitivity analysis of long-running landslide models through basis set expansion and meta-modelling
Rohmer, Jeremy
[INFO.INFO-MO] Computer Science [cs]/Modeling and Simulation
ART
Landslide modelling
Computationally intensive
Sobol' indices
Functional output
Meta-model
Principal component analysis
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.
2014
en
Natural Hazards
Springer Verlag