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

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.