Advertisement

Discovering Knowledge from Meteorological Databases: A Meteorological Aviation Forecast Study

  • Sérgio Viademonte
  • Frada Burstein
  • Robert Dahni
  • Scott Williams
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2114)

Abstract

In many application areas there are large historical data containing useful knowledge for decision support. However, this data taken in its raw form is usually of a poor quality. Thus it has very little value for the user-decision-maker if not adequately prepared. The Knowledge Discovery in Databases (KDD) is concerned with exploiting massive data sets in supporting use of historical data for decision-making. This paper describes an ongoing research project in the context of meteorological aviation forecasting, concerned with fog forecasting. The paper discusses the stages for performing knowledge discovery in the meteorological aviation-forecasting domain. The data used for research was taken from a real data set describing the aviation weather observations. The paper presents the data preprocessing stage, the discovered rules, achieved results and further directions of such research. We believe that this project can serve as a model for in a wider KDD-based decision support problem.

Keywords

Weather Observation Total Cloud Amount Generate Data Model Compass Point Meteorological Database 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Auer, A.H.J. (1992). Guidelines for Forecasting Fog. Part 1: Theoretical Aspects: Meteorological Service of NZ.Google Scholar
  2. Agrawal, R., Imielinski T.& Swami, A. (1993). Mining association rules between sets of items in large databases. In Proceedings of Conference Management of Data.Google Scholar
  3. Beckenkamp, F., Pree, W.& Feldens, M.A. (1998). Optimizations of the Combinatorial Neural Model. In Proceedings of 5th Brazilian Symposium on Neural Networks (SBRN’98), Belo Horizonte, Brazil.Google Scholar
  4. Buchner, A.G., Chan, J.C.L., Hung, S.L.& Hughes, J.G. (1998). A meteorological knowledgediscovery environment. Knowledge Discovery and Data Mining. (pp.204–226).Google Scholar
  5. Catlett, J. (1991). Megainduction: Machine learning on very large databases. UTS, Australia.Google Scholar
  6. Chen, F., Figlewski, S., Weigend, A.S.,& Waterhouse, S.R. (1998). Modeling financial data using clustering and tree-based approaches. In Proceedings of International Conference on Data Mining, (pp.35–51). Rio de Janeiro, Brazil: WIT Press.Google Scholar
  7. Fayyad, U.M., Mannila, H.& Ramakrishman, R. (1997). Data Mining and Knowledge Discovery (Vol.3). Boston: Kluwer.Google Scholar
  8. Gottgtroy, M.P.B., Rodrigues, M.J.N.& Sousa, M.T.G. (1998). Data mining agents. In Proc. of Intern.Conf on Data Mining (171–182). RiodeJaneiro, Brazil: WIT Press,Southampton, UK.Google Scholar
  9. Howard, C.M. & Rayward-Smith, V.J. (1998). Discovering Knowledge from low-quality meteorological databases., Knowledge Discovery and Data Mining. (pp.180–202.).Google Scholar
  10. Hruschka, E. & Ebecken, N. (1998). Rule Extraction from Neural Networks in Data Mining Applications. In Proc. of International Conference on Data Mining, (pp.303–314). RiodeJaneiro, Brazil: WIT Press, UK.Google Scholar
  11. Keith, R. (1991). Results And Recommendations Arising From An Investigation Into Forecasting Problems At Melbourne Airport. (MeteorologicalNote 195). Townsville: Bureau of Meteorology, Meteorological Office.Google Scholar
  12. Machado, R.J., Barbosa, V.C. & Neves, P.A. (1998). Learning in the Combinatorial Neural Model. IEEE Transactions on Neural Networks, 9. September 1998Google Scholar
  13. Mohammed, J.Z., Parthasarathy S., Li W.& Ogihara, M. (1996). Evaluation of Sampling for Data Mining of Association Rules. (Tech.Rep. 617). Rochester, New York: The University of Rochester, Comp. Sci.Dept.Google Scholar
  14. Piatetsky-Shapiro, G.,& Frawley, W. (1991). Knowledge Discovery in Databases.: MIT Press.Google Scholar
  15. Provost, F.,& Kolluri, V. (1999, June, 1999). A Survey of Methods for Scaling Up Inductive Algorithms. Data Mining and Knowledge Discovery, Volume 3, 131–169.CrossRefGoogle Scholar
  16. Pyle, D. (1999). Data Preparation for Data Mining. San Francisco, USA: Morgan Kaufmann Publishers, Inc.Google Scholar
  17. Quinlan, J.R. (1993). C4.5: Programs for Machine Learning. CA: Morgan Kaufmann.Google Scholar
  18. Siegler, W. & Steurer, E. (1998). Forecasting of the German stock index DAX with neural networks: Using daily data for experiments with input variable reduction and a modified error function. In Proc. of International Conference on Data Mining. (pp.289–301). RiodeJaneiro, Brazil: WIT Press, Southampton, UK.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Sérgio Viademonte
    • 1
  • Frada Burstein
    • 1
  • Robert Dahni
    • 2
  • Scott Williams
    • 2
  1. 1.School of Information Management and SystemsAustralia
  2. 2.Bureau of MeteorologyVictoriaAUSTRALIA

Personalised recommendations