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NPS

NPS. Robert DeFeo, chief horticulturalist for the National Park Service, is responsible for predicting when the cherry blossoms bloom. He has been making predictions and recording observations since 1992

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NPS

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  1. NPS • Robert DeFeo, chief horticulturalist for the National Park Service, is responsible for predicting when the cherry blossoms bloom. • He has been making predictions and recording observations since 1992 • Defined and recorded 6 phases of development: Green Color in Buds, Florets Visible, Extension of Florets, Peduncle Elongation, Puffy White, Peak Blooms

  2. NPS • DeFeo attempts to predict when the trees will reach peak bloom so that this will occur during the Cherry Blossom Festival • Default prediction is April 4th • He announces his prediction 2 weeks before the bloom

  3. NPS • What factors does DeFeo consider? • Bloom of other plants • High and low temperature • Photoperiod

  4. Collecting the Data • DeFeo provided bloom dates dating back to 1921 and dates of 6 stages back to 1992

  5. Collecting the Data • Weather data was obtained from the Weather Underground site • Data was pulled using screen scraping • Weather data started in 1948 • Incomplete data for 1948, 1994, 1995, 1996, 2000, and 2001.

  6. Data Collected

  7. Visualizing the Data • Year and average temperature in March

  8. Visualizing the Data • Year and Bloom Date

  9. Visualizing the Data • Bloom and temperature

  10. What can we learn from this? • Data “looks” linear • Strong correlation between temperature and when the peak bloom occurs

  11. Heuristics • GDD • Given by the equation: • (Thigh + Tlow)/2 - Tbaseline • Calculated accumulated GDD using values from 0°F to 60°F at 10° increments • Used linear regression • Found 0°F produced best adjusted R2 value.

  12. Calculated the accumulated GDD from Jan 1 to bloom date • Created program that reserved records for 26 of 53 years • Performed linear regression • Calculated RMSE on cross-validation set

  13. Heuristics • Regression: -0.0242095469666847x+88.8862936319553 • Total error: 7.2 • March 1st error: 5.7 • March 15th error: 5.8 • Bloom Date error: 8.5

  14. Heuristics • Used linear regression on same data but excluded January and February from regression model • Regression:-0.0216093918184858x+84.1086368228915 • Total error: 5.9 • March 1st error: 5.9 • March 15th error: 6.3 • Bloom Date error: 5.6

  15. Heuristics • Used linear regression on same data but excluded January from regression model • Regression: -0.0217918555433408x+85.0674318813992 • Total error: 6.2 • March 1st error: 6.6 • March 15th error: 6.2 • Bloom Date error: 5.9

  16. Heuristics • Recalculate GDD excluding without January and February • Regression: -0.0189188233985223x+33.4894170814518 • Total error: 5.7 • March 1st error: 6.9 • March 15th error: 6.4 • Bloom Date error: 6.4

  17. Calculate GDD beginning February 1st • Regression: -0.022215061163981x+61.0588653664869 • Total error: 5.8 • March 1st error: 6.1 • March 15th error: 5.8 • Peak Bloom error: 5.2

  18. Heuristics • Calculate GDD beginning February 1st. Create regression model starting March 1st. • Regression: -0.021961372553719x+59.6303409305679 • Total error: 5.5 • March 1st error: 5.3 • March 15th error: 5.0 • Peak Bloom error: 4.8

  19. Heuristics • Use GDD with ANN • Use accumulated GDD since January 1st as input • Preprocessed data to create a single lag-file for all the years • Processed data using CortexPro Neural Networks tool, v.5.0 • Days till bloom is output

  20. Total RMSE: 6.7

  21. Heuristics • Use average temperature as indicator of bloom date • Use linear regression on average temperature in March • Regression: -1.1550140341924x+147.033044143914 • RMSE: 4.8 • Use linear regression on average temperature of first 15 days in March • Regression:-0.505593057443286x+115.921494363343 • RMSE: 6.0

  22. Heuristics • Use average bloom date (April 4th) as prediction. • RMSE: 6.5

  23. Conclusions • Utility of model varies depending upon data available • While DeFeo’s model is accurate, powerful models were created that do not rely on direct observation of data • Models were “good enough” to fall into timespan of festival

  24. Future Work • The models created can be refined as the knowledge base grows • Include a standard measure of error for all models • Include photoperiod as a factor • Incorporate electronic GDD recordings • Include image data with pattern recognition

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