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James Brown

Brussels, 21/09/06. Results on assessing uncertainties in data and models. James Brown. brown.science@gmail.com. Overview. 1. The problem of uncertainty What aspects of data/models are uncertain? What aspects do we consider?. 2. Data Uncertainty Engine (DUE)

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James Brown

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  1. Brussels, 21/09/06 Results on assessing uncertainties in data and models James Brown brown.science@gmail.com

  2. Overview • 1. The problem of uncertainty • What aspects of data/models are uncertain? • What aspects do we consider? • 2. Data Uncertainty Engine (DUE) • What can DUE do? Concepts and functions. • Brief demonstration • 3. Exploitation of outputs • Ongoing and possible routes: research; applied

  3. 1. Problem of uncertainty

  4. Sources of uncertainty Model Params. Model states Input data Model structure Model output Data ± U Model ± U Output ± U

  5. Sources of uncertainty Model Params. Model states Input data Model structure Model output Data ± U Model ± U Output ± U

  6. 2. Data Uncertainty Engine (DUE)

  7. What can DUE do? • 1. Assessing (data) uncertainty • All types of objects and attributes • Probability models (for now) • Expert judgement and supporting (sample) data • User-friendly environment (structured interface) • 2. Propagating uncertainty • Uncertainties propagate through models • DUE uses ‘Monte Carlo simulation’ to assess this

  8. What can DUE do? • 3. Storing uncertainty • HarmoniRiB database is ‘uncertainty enabled’… • …download to DUE > add uncertainty > upload • Can also create and save projects to file (*.due)

  9. 3. Demonstration of DUE

  10. 4. Plans for exploitation

  11. Exploitation plans • 1. Academic/commercial research • Published; open source (free to use and modify) • Regularly updated; detailed plans for extensions • External collaboration: e.g. WL|Delft Hydraulics • 2. Practical applications (beyond Hrib) • Flood early warning with WL|Delft Hydraulics • Teaching (M.Sc. in Amsterdam and Wageningen) • Very open to suggested collaboration/outreach??

  12. ????

  13. Uncertain objects 1. Single point object 2a. Multi-point: ‘rigid’ 2b. Multi-point ‘deformable’ Rigid origin

  14. Multi-point objects 2a. Rigid 2b. Deformable

  15. Classification of attributes A. Continuous Numerical B. Discrete Numerical C. Categorical A1 B1 C1 1. Time A2 B2 C2 2. Space A3 B3 C3 3. Neither A4 B4 C4 4. Both

  16. Sources of uncertainty Data ± U Testing data Model Params. Model states Input data Approx. solution Model structure Model output Data ± U Model ± U Output ± U

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