Trevor Page, Paul Smith, Keith Beven, Francesca Pianosi, Fanny Sarrazin, Suzanna Almeida, Liz Holcome, Jim Freer and Thorsten Wagener
- The CREDIBLE Uncertainty Estimation (CURE) Toolbox is an open source MATLAB™ toolbox for uncertainty estimation aimed at scientists and practitioners who are not necessarily experts in uncertainty estimation. The toolbox focusses on environmental simulation models and employs a range of different Monte Carlo methods for uncertainty estimation. The methods are demonstrated with a range of modelling applications within workflow scripts. The scripts provide examples of how to utilise the CURE toolbox functions to help users definine their own workflow. The toolbox implementation aims to increase the uptake of uncertainty estimation methods within a framework designed to be open and explicit as well as to represent best practice in applying the methods included. Best practice in the evaluation of modelling assumptions and choices is included by the incorporation of a condition tree that allows users to record modelling assumptions and choices made. The assumptions and choices can be captured via use of a GUI which are subsequently recorded as an audit trail log. CURE has been developed in parallel with the sensitivity analysis toobox SAFE.
- CURE provides a set of Workflow Scripts that run functions to perform uncertainty estimation for a variety of different applications. The toolbox was developed as part of the CREDIBLE (Consortium on Risk in the Environment: Diagnostics, Integration, Benchmarking, Learning, and Elicitation) project to improve the handling of uncertainty for scientists and stakeholders.
CURE includes several methods including:
- Forward Uncertainty Estimation
- Generalised Likelihood Uncertainty Estimation (GLUE)
- Bayesian Statistical Methods
- as well as multiple methods for sampling the parameter space.
CURE has been developed for the MATLAB™ programming environment
- The development of CURE was supported by the UK Natural Environment Research Council through the Consortium on Risk in the Environment: Diagnostics, Integration, Benchmarking, Learning and Elicitation (CREDIBLE) [NE/J017450/1].