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.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
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)
Rulequest Research. An overview of Cubist, http://rulequest.com/cubist-win.html
Rice, J.R.: The algorithm selection problem. Advances in Computers 15, 65–118 (1976)
Smith-Miles, K.A.: Cross-Disciplinary perspectives on meta-learning for algorithm selection. ACM Computing Surveys 41, 1–25 (2008)
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)
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)
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)
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)
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)
Palmer, W.C.: Meteorological Drought. Research Paper No. 45, U.S. Department of Commerce Weather Bureau, Washington, D.C. p. 58 (1965)
Wells, N., Goddard, S., Hayes, M.J.: A Self-Calibrating Palmer Drought Severity Index. Journal of Climate 17(12), 2335–2351 (2004)
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)
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)
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)
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)
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)
Witten, I., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques, 2nd edn. Morgan Kaufmann, San Francisco (2005)
Rousseeuw, P.J., Leroy, A.M.: Robust Regression and Outlier Detection. John Wiley & Sons, Inc., Chichester (1987)
Quinlan, R.J.: Learning with Continuous Classes. In: 5th Australian Joint Conference on Artificial Intelligence, Singapore, pp. 343–348 (1992)
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)
Holmes, G., Hall, M., Frank, E.: Generating Rule Sets from Model Trees. In: Twelfth Australian Joint Conference on Artificial Intelligence, pp. 1–12 (1999)
Breiman, L.: Bagging predictors. Machine Learning. 24(2), 123-140 (1996)
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)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights 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)