Assessing local structural identifiability for environmental models

• Published in: Environmental modelling & software : with environment data news 2017-07, Vol.93, p.398-408
• Main Authors: Stigter, J.D., Beck, M.B., Molenaar, J.
• Format: Article
• Language: English
• Subjects: Environment models; Environmental impact; Environmental modeling; Identifiability; Identification methods; Microbial degradation; Microorganisms; Model reduction; Non-linear parameter estimation; Parameter identification; Parameter sensitivity; Parametric output sensitivity; Sensitivity analysis; Singular value decomposition; Studies; Uncertainty analysis
• ISSN: 1364-8152; 1873-6726
• DOI: 10.1016/j.envsoft.2017.03.006

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. •A novel algorithm to assess local structural identifiability of non-linear state-space models is applied.•The concept ‘Identifiability Signature’ is introduced.•Both practical and theoretical identifiability are investigated for a microbial degradation model.•Re-parametrisations of the model are presented for various sensor combinations.