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Research Papers

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

Authors:

Mohamed Gebri ,

Autonomous Control and Information Technology Center, Department of Electrical and Computer Engineering, North Carolina A & T State University, Greensboro, NC 27411
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Eric Kihn,

Eyad Haj Said,

University of Kalamoom, Deratiah, Syria
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Abdollah Homaifar

Autonomous Control and Information Technology Center, Department of Electrical and Computer Engineering, North Carolina A & T State University, Greensboro, NC 27411
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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.
DOI: http://doi.org/10.2481/dsj.009-004
How to Cite: Gebri, M. et al., (2011). Detecting Environmental Change Using Self-Organizing Map Techniques Applied to the ERA-40 Database. Data Science Journal. 10, pp.1–12. DOI: http://doi.org/10.2481/dsj.009-004
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Published on 11 May 2011.
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