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
Eyad Haj Said,
University of Kalamoom, Deratiah, Syria
Abdollah Homaifar
Autonomous Control and Information Technology Center, Department of Electrical and Computer Engineering,
North Carolina A & T State University, Greensboro, NC 27411
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.
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
Published on 11 May 2011.
Peer Reviewed
Downloads
