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

Data Science Journal

Eric Kihn¹  Abdollah Homaifar¹  Eyad Haj Said²  Mohamed Gebril¹ 

[1] Autonomous Control and Information Technology Center, Department of Electrical and Computer Engineering, North Carolina A & T State University;University of Kalamoom, Deratiah, Syria

关键词: Meteorological Database;    Data Mining;    Clustering;    Self Organizing Map;    ERA-40;   

DOI  :  10.2481/dsj.009-004

学科分类：计算机科学（综合）

来源: Ubiquity Press Ltd.

【 摘 要 】

References(32)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.

【 授权许可】

Unknown