Affordable Access

Nonparametric estimation of aggregated Sobol' indices: application to a depth averaged snow avalanche model

Authors
  • Belén Heredia, María
  • Prieur, Clémentine
  • Eckert, Nicolas
Publication Date
Jun 15, 2020
Source
HAL-Descartes
Keywords
Language
English
License
Unknown
External links

Abstract

Avalanche models are increasingly employed for elaborating land-use maps and designing defense structures, but they rely on poorly known parameters. Careful uncertainty assessment is thus required but difficulty arises from the nature of the outputs of these models, which are commonly both functional and scalar. Hence, so far in the avalanche field, few sensitivity analyses have been performed. In this work, we propose to determine the most influential inputs of an avalanche model by estimating aggregated Sobol' indices. We propose a nonparametric estimation procedure based on the Nadaraya-Watson kernel smoother, which allows to estimate the aggregated Sobol' indices from a given random sample of small to moderate size. Due to the limited size of the sample, the kernel estimation is biased. Therefore , we propose a bootstrap based bias correction before selecting the bandwidth by cross-validation. After different test-cases showing the efficiency of our approach, it is applied to a real avalanche case. Results show that the friction parameters and the snow depth in the release zone are the most influential parameters determining the avalanche characteristics.

Report this publication

Statistics

Seen <100 times