Classification of meteorological volumetric radar data using rough set methods

  • Authors:

  • J. F. Peters;Z. Suraj;S. Shan;S. Ramanna;W. Pedrycz;N. Pizzi

  • Affiliations:

  • Department of Electrical and Computer Engineering, University of Manitoba, 15 Gillson Street, ENGR 504, Winnipeg, MB, Canada R3T 5V6;University of Information Technology and Management, H. Sucharskiego 2, 35-225 Rzeszów, Poland;Department of Electrical and Computer Engineering, University of Manitoba, 15 Gillson Street, ENGR 504, Winnipeg, MB, Canada R3T 5V6;Department of Electrical and Computer Engineering, University of Manitoba, 15 Gillson Street, ENGR 504, Winnipeg, MB, Canada R3T 5V6;Institute for Biodiagnostics, National Research Council, Winnipeg, MB, Canada R3B 1Y6;Computer Engineering, University of Alberta, Edmonton, AB, Canada T6G 2G7

  • Venue:
  • Pattern Recognition Letters - Special issue: Rough sets, pattern recognition and data mining
  • Year:
  • 2003

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Abstract

This paper reports on a rough set approach to classifying meteorological volumetric radar data used to detect storm events responsible for summer severe weather. The classification of storm cells is a difficult problem due to the complex evolution of storm cells, the high dimensionality of the weather data, and the imprecision and incompleteness of the data. A rough set approach is used to classify different types of meteorological storm events. A considerable of different classification strategies techniques have been considered and compared to determine which approach will best classify the volumetric storm cell data coming from the Radar Decision Support System database of Environment Canada. The criterion for comparison is the accuracy coefficient in the classification over a testing data. The contribution of this paper is a new application of rough set theory in classifying meteorological radar data.