Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Record number 552694
Title Introductory overview of identifiability analysis: A guide to evaluating whether you have the right type of data for your modeling purpose
Author(s) Guillaume, Joseph H.A.; Jakeman, John D.; Marsili-Libelli, Stefano; Asher, Michael; Brunner, Philip; Croke, B.; Hill, Mary C.; Jakeman, Anthony J.; Keesman, Karel J.; Razavi, S.; Stigter, Johannes D.
Source Environmental Modelling & Software 119 (2019). - ISSN 1364-8152 - p. 418 - 432.
DOI https://doi.org/10.1016/j.envsoft.2019.07.007
Department(s) Mathematical and Statistical Methods - Biometris
WIMEK
VLAG
PE&RC
Publication type Refereed Article in a scientific journal
Publication year 2019
Keyword(s) Derivative based methods - Emulation - Hessian - Identifiability - Non-uniqueness - Response surface - Uncertainty
Abstract

Identifiability is a fundamental concept in parameter estimation, and therefore key to the large majority of environmental modeling applications. Parameter identifiability analysis assesses whether it is theoretically possible to estimate unique parameter values from data, given the quantities measured, conditions present in the forcing data, model structure (and objective function), and properties of errors in the model and observations. In other words, it tackles the problem of whether the right type of data is available to estimate the desired parameter values. Identifiability analysis is therefore an essential technique that should be adopted more routinely in practice, alongside complementary methods such as uncertainty analysis and evaluation of model performance. This article provides an introductory overview to the topic. We recommend that any modeling study should document whether a model is non-identifiable, the source of potential non-identifiability, and how this affects intended project outcomes.

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