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)


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.


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.


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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

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