Assessing local structural identifiability for environmental models |
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| Affiliation: | 1. Biometris, Wageningen University and Research, Wageningen, The Netherlands;2. Department of Civil and Environmental Engineering, Imperial College, South Kensington Campus, London, United Kingdom;1. Unité Modèles pour l’Ecotoxicologie et la Toxicologie (METO), Institut National de l’Environnement Industriel et des Risques (INERIS), BP2, F-60550 Verneuil en Halatte, France;2. Institut de Radioprotection et de Sûreté Nucléaire (IRSN), PRP-ENV, SERIS, Laboratoire d’ECOtoxicologie des radionucléides (LECO), Cadarache, France;1. Ifremer, STH/LBH, B.P. 70, 29280 Plouzané, France;2. Ifremer, EMH, rue de l’île d’Yeu, B.P. 21105, 44311 Nantes Cedex 03, France;1. Department of Mathematics, University of Wyoming, Laramie, WY 82071, USA;2. Department of Zoology and Physiology, University of Wyoming, Laramie, WY 82071, USA;3. U.S. Geological Survey and Department of Biology, University of Miami, Coral Gables, FL 33124, USA;4. Institute of Arctic Biology, University of Alaska, Fairbanks, AK 99775, USA;1. USDA-ARS, U.S. Arid Land Agricultural Research Center, 21881 N Cardon Ln, Maricopa, AZ, 85138, USA;2. USDA-ARS, Conservation and Production Research Laboratory, 2300 Experiment Station Rd, Bushland, TX, 79012, USA;3. USDA-ARS, Center for Agricultural Resources Research, 2150 Centre Ave, Fort Collins, CO, 80526, USA;4. USDA-ARS, Cropping Systems Research Laboratory, 3810 4th St, Lubbock, TX 79415, USA |
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| 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. |
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| Keywords: | Identifiability Non-linear parameter estimation Parametric output sensitivity Model reduction |
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