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Research Papers

Detecting Environmental Change Using Self-Organizing Map Techniques Applied to the ERA-40 Database

Authors
  • Mohamed Gebri
  • Eric Kihn
  • Eyad Haj Said
  • Abdollah Homaifar

Abstract

Data mining is a valuable tool in meteorological applications. Properly selected data mining techniques enable researchers to process and analyze massive amounts of data collected by satellites and other instruments. Large spatial-temporal datasets can be analyzed using different linear and nonlinear methods. The Self-Organizing Map (SOM) is a promising tool for clustering and visualizing high dimensional data and mapping spatial-temporal datasets describing nonlinear phenomena. We present results of the application of the SOM technique in regions of interest within the European re-analysis data set. The possibility of detecting climate change signals through the visualization capability of SOM tools is examined.
Year: 2011
Volume 10
Page/Article: 1-12
DOI: 10.2481/dsj.009-004
Published on May 11, 2011
Peer Reviewed