Practical identifiability analysis of environmental models
Stefano Marsili-Libelli, Philip Brunner, Barry Croke, Joseph Guillaume, Anthony J. Jakeman, John D. Jakeman, Karel J. Keesman & Johannes D. Stigter
| Abstract |
Identifiability of a system model can be considered as the extent to
which one can capture its parameter values from observational data
and other prior knowledge of the system. Identifiability must be
considered in context so that the objectives of the modelling must
also be taken into account in its interpretation. A model may be
identifiable for certain objective functions but not others; its
identifiability may depend not just on the model structure but also
on the level and type of noise, and may even not be identifiable
when there is no noise on the observational data. Context also
means that non-identifiability might not matter in some contexts,
such as when representing pluralistic values among stakeholders,
and may be very important in others, such as where it leads to
intolerable uncertainties in model predictions. Uncertainty
quantification of environmental systems is receiving increasing
attention especially through the development of sophisticated
methods, often statistically-based. This is partly driven by the
desire of society and its decision makers to make more informed
judgments as to how systems are better managed and associated
resources efficiently allocated. Less attention seems to be given
by modellers to understand the imperfections in their models and
their implications. Practical methods of identifiability analysis
can assist greatly here to assess if there is an identifiability
problem so that one can proceed to decide if it matters, and if so
how to go about modifying the model ( transforming parameters,
selecting specific data periods, changing model structure, using a
more sophisticated objective function). A suite of relevant methods
is available and the major useful ones are discussed here including
sensitivity analysis, response surface methods, model emulation and
the quantification of uncertainty. The paper also addresses various
perspectives and concepts that warrant further development and
use. |
| Keywords |
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| Citation | Marsili-Libelli, S., Brunner, P., Croke, B., Guillaume, J., Jakeman, A. J., Jakeman, J. D., Keesman, K. J., & Stigter, J. D. (2014). Practical identifiability analysis of environmental models. Paper presented at 7th International Congress on Environmental Modelling and Software, San Diego, California, USA. |
| Type | Conference paper (English) |
| Name of conference | 7th International Congress on Environmental Modelling and Software (San Diego, California, USA) |
| Date of conference | 2014 |
| Publisher | IEMSS society |
| Pages | 1-12 |