Skip to main content

Algorithm and Feature Selection for VegOut: A Vegetation Condition Prediction Tool

  • Conference paper
Discovery Science (DS 2009)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5808))

Included in the following conference series:

  • 1936 Accesses

Abstract

The goal of the VegOut tool is to provide accurate early warning drought prediction. VegOut integrates climate, oceanic, and satellite-based vegetation indicators to identify historical patterns between drought and vegetation conditions indices and predict future vegetation conditions based on these patterns at multiple time steps (2, 4 and 6-week outlooks). This paper evaluates different sets of data mining techniques and various climatic indices for providing the improved prediction accuracy to the VegOut tool.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Tadesse, T., Wardlow, B.: The Vegetation Outlook (VegOut): A New Tool for Providing Outlooks of General Vegetation Conditions Using Data Mining Techniques. In: 7th IEEE International Conference on Data Mining Workshops, pp. 667–672. IEEE Press, Washington (2007)

    Google Scholar 

  2. Rulequest Research. An overview of Cubist, http://rulequest.com/cubist-win.html

  3. Rice, J.R.: The algorithm selection problem. Advances in Computers 15, 65–118 (1976)

    Article  Google Scholar 

  4. Smith-Miles, K.A.: Cross-Disciplinary perspectives on meta-learning for algorithm selection. ACM Computing Surveys 41, 1–25 (2008)

    Article  Google Scholar 

  5. Wilhite, D.: Preparing for drought: a methodology. In: Wilhite, D.A. (ed.) Drought: A Global Assessment. Routledge Hazards and Disaster Series, vol. II, pp. 89–104 (2002)

    Google Scholar 

  6. Wilhite, D., Svoboda, M.: Drought Early Warning Systems in the Context of Drought Preparedness and Mitigation. Preparedness and Mitigation Proceedings of an Expert Group, Lisbon, Portugal (2000)

    Google Scholar 

  7. Tadesse, T., Brown, J.F., Hayes, M.J.: A new approach for predicting drought-related vegetation stress: Integrating satellite, climate, and biophysical data over the U.S. central plains. ISPRS Journal of Photogrammetry and Remote Sensing 59(4), 244–253 (2005)

    Article  Google Scholar 

  8. Bayarjargala, Y., Karnieli, A., Bayasgalan, M., Khudulmurb, S., Gandush, C., Tucker, C.J.: A comparative study of NOAA–AVHRR derived drought indices using change vector analysis. Remote Sensing of Environment 105(1), 9–22 (2006)

    Article  Google Scholar 

  9. Tadesse, T., Wilhite, D.A., Harms, S.K., Hayes, M.J., Goddard, S.: Drought monitoring using data mining techniques: A case study for Nebraska, U.S.A. Natural Hazards Journal 33, 137–159 (2004)

    Article  Google Scholar 

  10. Palmer, W.C.: Meteorological Drought. Research Paper No. 45, U.S. Department of Commerce Weather Bureau, Washington, D.C. p. 58 (1965)

    Google Scholar 

  11. Wells, N., Goddard, S., Hayes, M.J.: A Self-Calibrating Palmer Drought Severity Index. Journal of Climate 17(12), 2335–2351 (2004)

    Article  Google Scholar 

  12. McKee, T.B., Doesken, N.J., Kleist, J.: Drought Monitoring with Multiple Time Scales. In: Preprints, 9th Conference on Applied Climatology, Dallas, Texas, January 15–20, pp. 233–236 (1995)

    Google Scholar 

  13. Reed, B.C., Brown, J.F., VanderZee, D., Loveland, T.R., Merchant, J.W., Ohlen, D.O.: Measuring phonological variability from satellite imagery. Journal of Vegetation Science 5, 703–714 (1994)

    Article  Google Scholar 

  14. Barnston, A.G., Kumar, A., Goddard, L., Hoerling, M.P.: Improving seasonal prediction practices through attribution of climate variability. Bull. Amer. Meteor. Soc. 86, 59–72 (2005)

    Article  Google Scholar 

  15. Tadesse, T., Wilhite, D.A., Hayes, M.J., Harms, S.K., Goddard, S.: Discovering associations between climatic and oceanic parameters to monitor drought in Nebraska using data-mining techniques. Journal of Climate 18(10), 1541–1550 (2005)

    Article  Google Scholar 

  16. Los, S.O., Collatz, G.J., Bounoua, L., Sellers, P.J., Tucker, C.J.: Global Interannual Variations in Sea Surface Temperature and Land Surface Vegetation, Air Temperature, and Precipitation. Journal of Climate, 1535–1549 (2001)

    Google Scholar 

  17. Witten, I., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques, 2nd edn. Morgan Kaufmann, San Francisco (2005)

    MATH  Google Scholar 

  18. Rousseeuw, P.J., Leroy, A.M.: Robust Regression and Outlier Detection. John Wiley & Sons, Inc., Chichester (1987)

    Book  MATH  Google Scholar 

  19. Quinlan, R.J.: Learning with Continuous Classes. In: 5th Australian Joint Conference on Artificial Intelligence, Singapore, pp. 343–348 (1992)

    Google Scholar 

  20. Wang, Y., Witten, I.H.: Induction of model trees for predicting continuous classes. In: Poster papers of the 9th European Conference on Machine Learning (1997)

    Google Scholar 

  21. Holmes, G., Hall, M., Frank, E.: Generating Rule Sets from Model Trees. In: Twelfth Australian Joint Conference on Artificial Intelligence, pp. 1–12 (1999)

    Google Scholar 

  22. Breiman, L.: Bagging predictors. Machine Learning. 24(2), 123-140 (1996)

    Google Scholar 

  23. Shevade, S.K., Keerthi, S.S., Bhattacharyya, C., Murthy, K.R.K.: Improvements to the SMO Algorithm for SVM Regression. IEEE Transactions on Neural Networks (1999)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Harms, S., Tadesse, T., Wardlow, B. (2009). Algorithm and Feature Selection for VegOut: A Vegetation Condition Prediction Tool. In: Gama, J., Costa, V.S., Jorge, A.M., Brazdil, P.B. (eds) Discovery Science. DS 2009. Lecture Notes in Computer Science(), vol 5808. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04747-3_11

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-04747-3_11

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-04746-6

  • Online ISBN: 978-3-642-04747-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics