Empirical comparison of correlation measures and pruning levels in complex networks representing the global climate system

Pelan, A. and Steinhaeuser, K. and Chawla, N.V. and de Alwis Pitts, D. A. and Ganguly, A.R. (2011) Empirical comparison of correlation measures and pruning levels in complex networks representing the global climate system. In: 2011 IEEE Symposium on Computational Intelligence and Data Mining (CIDM), 11-15 April 2011, Paris, France.

Full content URL: https://www.scopus.com/inward/record.uri?eid=2-s2....

Documents
Empirical comparison of correlation measures and pruning levels in complex networks representing the global climate system
[img]
[Download]
[img]
Preview
PDF
Network.pdf - Whole Document

584kB
Item Type:Conference or Workshop contribution (Presentation)
Item Status:Live Archive

Abstract

Climate change is an issue of growing economic, social, and political concern. Continued rise in the average temperatures of the Earth could lead to drastic climate change or an increased frequency of extreme events, which would negatively affect agriculture, population, and global health. One way of studying the dynamics of the Earth's changing climate is by attempting to identify regions that exhibit similar climatic behavior in terms of long-term variability. Climate networks have emerged as a strong analytics framework for both descriptive analysis and predictive modeling of the emergent phenomena. Previously, the networks were constructed using only one measure of similarity, namely the (linear) Pearson cross correlation, and were then clustered using a community detection algorithm. However, nonlinear dependencies are known to exist in climate, which begs the question whether more complex correlation measures are able to capture any such relationships. In this paper, we present a systematic study of different univariate measures of similarity and compare how each affects both the network structure as well as the predictive power of the clusters. © 2011 IEEE.

Additional Information:cited By 5; Conference of Symposium Series on Computational Intelligence, IEEE SSCI2011 - 2011 IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2011 ; Conference Date: 11 April 2011 Through 15 April 2011; Conference Code:85933
Keywords:Average temperature, Changing climate, Community detection algorithms, Complex correlation, Complex networks, Correlation measures, Cross correlations, Descriptive analysis, Emergent phenomenon, Empirical comparison, Extreme events, Global climate system, Global health, Long-term variability, Network structures, Nonlinear dependencies, Predictive modeling, Predictive power, Systematic study, Univariate, Artificial intelligence, Data mining, Earth (planet), Climate change
Subjects:G Mathematical and Computer Sciences > G150 Mathematical Modelling
Divisions:College of Science > School of Geography
ID Code:29403
Deposited On:24 Oct 2018 13:24

Repository Staff Only: item control page