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Distilling Free-Form Ecological Theory Using High Frequency Data

Distilling Free-Form Ecological Theory Using High Frequency Data . Kevin Rose. Model Discovery. Applications/needs – Data sets large, difficult to manage – how can we identify patterns amongst millions of data points? Complexity – can we visually identify all relationships ourselves?

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Distilling Free-Form Ecological Theory Using High Frequency Data

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  1. Distilling Free-Form Ecological Theory Using High Frequency Data Kevin Rose

  2. Model Discovery • Applications/needs – • Data sets large, difficult to manage – how can we identify patterns amongst millions of data points? • Complexity – can we visually identify all relationships ourselves? • Weaknesses – • Not question driven • Only identifies relationships, not causality • Hard to deal with hysteresis

  3. Eureqa software

  4. 320 nm UV light transparency data Lake Tahoe, CA/NV (MLTP site) December 6th, 2006

  5. 320 nm UV light transparency data Lake Tahoe, CA/NV December 6th, 2006 ??? Let’s work backwards Let’s pretend we don’t know this

  6. 320 nm UV light transparency data Lake Tahoe, CA/NV (MLTP site) December 6th, 2006

  7. Genetic algorithms Symbolic regression This took my PC 26 seconds

  8. Hampen sø

  9. Hampen sø

  10. Hampen sø

  11. Hampen sø

  12. Hampen sø The model Input parameters (11) Day of year Temp at 0.5, 1, 2, 3, 4, 5, 7, 9m PAR Wind Speed

  13. Hampen sø The model Input parameters (11) Day of year Temp at 0.5, 1, 2, 3, 4, 5, 7, 9m PAR Wind Speed Temp difference between 1m and 5m Wind Speed Surface water temperature Temp at 4m Only important when large Important model component Epi - hypo metalimnion Strength of stability Diel fluctuations

  14. Hampen sø

  15. Hampen sø

  16. Hampen sø

  17. Hampen sø

  18. Hampen sø

  19. Hampen sø

  20. Grib sø

  21. Grib sø

  22. Hampen sø The model Input parameters (11) Day of year Temp at 0.5, 1, 2, 3, 4, 5, 7, 9m PAR Wind Speed 8.0 Temp difference between 1m and 5m Wind Speed Surface water temperature Temp at 4m Only important when large Important model component Epi - hypo metalimnion Strength of stability Diel fluctuations

  23. Grib sø

  24. Grib sø

  25. Grib sø

  26. Phycocyanin • Can we predict phycocyanin signal?

  27. Phycocyanin

  28. Chlorophyll

  29. Input parameters: • Chlorophyll • DO • Air temp • Day of year • Temp at 0.0,0.5,1,1.5,2,3,4,5,6,7,8,10,11,12, 13,14,15,16,17,18,19m • Best model used chlorophyll, DO, temp at 0.5m • Some success – R2 = 0.78 over ~3 weeks.

  30. What now • Ken Chiu & students (SUNY Binghamton) currently developing GLEON version of Eureqa • Issues with Eureqa – covariation, independence, causality • Eureqa free to download, easy to use – recommended you give it a try • Next steps?

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