1 / 16

Fuzzy knowledge-based curve evaluation for 1-D river model calibration

1 2. Hydrology-Hydraulics Research Unit Orion Project. Fuzzy knowledge-based curve evaluation for 1-D river model calibration. Jean-Philippe Vidal 1 Sabine Moisan 2. Outline. Formalisation of the calibration process Steps and subtasks Classes and objects CaRMA - 1 Architecture

wfleming
Download Presentation

Fuzzy knowledge-based curve evaluation for 1-D river model calibration

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. 1 2 Hydrology-Hydraulics Research Unit Orion Project Fuzzy knowledge-based curve evaluation for 1-D river model calibration Jean-Philippe Vidal1 Sabine Moisan2

  2. Outline • Formalisation of the calibration process • Steps and subtasks • Classes and objects • CaRMA-1 • Architecture • Output comparison subtask • Curve evaluation module • Approach • Implementing and using dictionaries • Interpreting symbolic discrepancies Page 2

  3. Numerical optimization Fast Reproducible Sometimes misleading Efficient Manual calibration Time-consuming Hardly transferable Reliable Effective What do you mean by “model calibration”? Aim: gathering advantages from both approaches byautomating “expert” manual calibration But model calibration is not only parameter fitting… Page 3

  4. Seven main generic steps: Data assignment Parameter definition Parameter initialisation Simulation run Output comparison Parameter adjustment Model performance description Formalization of the calibration process What kind of parameters should be calibrated? And how many of them? To what extent do model predictions agree with reference data? In what respect is the model good after calibration? What are the best prior estimates of parameter values? What is the physically acceptable range of each parameters? How to create input files? How to run a simulation? How to extract relevant outputs? Which data can be used as inputs to perform simulations? Among data left, which ones should be taken as references? Should resistance coefficients be adjusted individually or as a whole? Should this weir discharge coefficient be adjusted? 1. Formalisation Page 4

  5. Formalization of the calibration process 1. Formalisation Page 5

  6. CaRMA-1, a knowledge-based support system • Knowledge base: class definitions, task structures, and task implementations • At a generic level • In 1D hydraulics • Fact base: class instantiations relative to the specific model and the physical system under study • Inference engine uses symbolic information held in the FB and KB to perform all calibration subtasks: • Most of them automatically • Interactive guidance provided for others 2. CaRMA-1 Page 6

  7. Zoom on the output comparison step • Central subtask draws a comparison between: • a single reference: set of 2D points • a single prediction: 2D curve • Subtask currently performed interactively through closed questions about graphical discrepancies 2. CaRMA-1 Page 7

  8. Curve evaluation module • Aim:automating visual inspection for curve description and comparison • Approach:formalizing expert visual assessment • Use: • Stand–alone application • Integration within CaRMA: transparent use in the symbolic reasoning process 3. Curve module Page 8

  9. Symbolic World Numeric World Identifying discrepancies Fuzzy description Predicted numeric curve Symbolic Curve Fuzzy comparison Reference set of points Relative distance and position 3. Curve module Page 9

  10. WSP_dic … Far above 20 30 40 50 … Far above +20 +30 +40 +50 Dictionaries: implementation • Correspondence numeric-symbolic for: • Description of curve elements (segments, peaks, slope breaks) • Comparison features • Expert-defined: “capitalization” of expertise • Specific of a curve type “Far above” means “Probably between +30 cm and +40 cm, certainly between +20 cm and +50 cm” 3. Curve module Page 10

  11. WSP_dic … Significantly above w1 w2w3 w4 Far above x1 x2x3 x4 … 0.8 Significantly above Far above 0.2 Dictionaries: use Transparent conversion from numerical data to fuzzy symbolic discrepancies +22cm “Significantly above” with grade 0.8 And “Far above” with grade 0.2 3. Curve module Page 11

  12. Interpreting symbolic discrepancies • Nature of reference data • Domain of intended application Examples of common combinations • Performance criteria Page 12

  13. Discussion • Dictionaries have to be validated against an extensive data set by a panel of experts • Module tested with basic dictionaries on validation data sets for CARMA-1 • Generic way to derive qualitative comparisons Page 13

  14. Conclusions • Curve evaluation module: relevant alternative to numerical goodness-of-fit criteria • Appropriate complement to existing calibration support system • Contributes to make the calibration process both reliable and reproducible Good calibration practice Page 14

  15. Contacts • Thank you! • E-mail • jpv@hrwallingford.co.uk • PhD thesis available on-line • http://www.lyon.cemagref.fr/doc/these/vidal Page 15

  16. First level of the calibration process Further decomposition into 27 subtasks Page 16

More Related