Capturing system dynamics using complex networks and granger causality analysis: Application to environmental data

Abstract

In the present study, we introduce a novel methodology to analyze environmental observations. Environmental phenomena are usually complex and the majority of them present nonlinear behavior. In the first part of the present work we focus on understanding the underlying dynamics a turbulent heated jet through the study of temperature time series recorded along a horizontal line through the jet axis, we transformed time series into networks and evaluated the topological properties of the networks using complex network time series analysis. The results show that the complex network approach allows distinguishing and identifying in a quite detailed way the various dynamical regions of the jet flow. In the second part we analyzed environmental time series (Atmospheric pressure, Air temperature, Wind speed, Water temperature, Current speed and Wave height) from Seawatch buoys in the Aegean and Ionian Sea, maintained by Hellenic Center for Marine Research (HCMR) in the framework of the POSEIDON project. In order to examine the cause - effect relationship between the environmental variables we employed the cross-correlation and Granger causality methodologies. The results show that there are correlations and causalities between variables. Through the performance of networks measures and Granger causality analysis, we show that these methodologies are able to capture and characterize underlying system dynamics, and associate them to the corresponding physical behavior.