Introductory overview of identifiability analysis : A guide to evaluating whether you have the right type of data for your modeling purpose

Research output: Contribution to journalReview ArticleScientificpeer-review

Researchers

  • Joseph Guillaume

  • John D. Jakeman
  • Stefano Marsili-Libelli
  • Michael Asher
  • Philip Brunner
  • B. Croke
  • Mary C. Hill
  • Anthony J. Jakeman
  • Karel J. Keesman
  • S. Razavi
  • Johannes D. Stigter

Research units

  • Australian National University
  • Sandia National Laboratories NM
  • University of Florence
  • University of Neuchatel
  • University of Kansas
  • Wageningen University & Research
  • University of Saskatchewan

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.

Details

Original languageEnglish
Pages (from-to)418-432
Number of pages15
JournalEnvironmental Modelling and Software
Volume119
Publication statusPublished - 1 Sep 2019
MoE publication typeA2 Review article in a scientific journal

    Research areas

  • Derivative based methods, Emulation, Hessian, Identifiability, Non-uniqueness, Response surface, Uncertainty

ID: 35772530