Assessing local structural identifiability for environmental models

J.D. Stigter*, M.B. Beck, Jaap Molenaar

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

7 Citations (Scopus)

Abstract

The local structural identifiability problem is investigated for the general case and demonstrated for a well-known microbial degradation model that includes 13 unknown parameters and 3 additional states. We address the identifiability question using a novel algorithm that can be used for large models with many parameters to be identified. A key ingredient in the analysis is the application of a singular value decomposition of the normalized parametric output sensitivity matrix that is obtained through a simple model integration. The SVD results are further analysed and verified in a complementary symbolic computation. It is especially the swiftness and accuracy of the suggested method that we consider to be a substantial advantage in comparison to existing methods for a structural identifiability analysis. The method also opens, in a natural way, the analysis of (parametric) uncertainty in general, and this is demonstrated in more detail in the results section.
Original languageEnglish
Pages (from-to)398-408
JournalEnvironmental Modelling & Software
Volume93
DOIs
Publication statusPublished - 2017

Keywords

  • Identifiability
  • Model reduction
  • Non-linear parameter estimation
  • Parametric output sensitivity

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