An evaluation list as model selection aid: finding models with a balance between modelcomplexity, data availability and model application

Research output: Contribution to conferenceAbstract

Abstract

The continuous increase in the complexity of models that are being applied for environmental assessments results in increased uncertainty about the quantitative predictions. Classical criteria to find optimal models, such as the Akaike information criterion, do not consider the application. A list that evaluates the balance between model complexity, data support, and application, gives different ‘optimal’ models than classical criteria. This is joint work with P.W. Bogaart.
Original languageEnglish
Publication statusPublished - 2013
EventAll models are wrong....Groningen, the Netherlands -
Duration: 14 Mar 201116 Mar 2011

Conference

ConferenceAll models are wrong....Groningen, the Netherlands
Period14/03/1116/03/11

Fingerprint Dive into the research topics of 'An evaluation list as model selection aid: finding models with a balance between modelcomplexity, data availability and model application'. Together they form a unique fingerprint.

Cite this