Chaotic Dynamics of Environmental and Hydrological Time Series
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Environmental Sciences".
Deadline for manuscript submissions: closed (20 February 2022) | Viewed by 2677
Special Issue Editor
Interests: water and life; anthropogenic changes in the hydrological cycle; trends in hydrological variables; hydrological modeling; drinking water; health; food and energy nexus; water-related disasters; water-related conflicts
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Special Issue Information
Dear Colleagues,
The basic objective of time series analysis is to understand the characteristics of processes that generate time series and to make future predictions as well as simulations under different scenarios. Certain types of processes which are seemingly stochastic are in fact evolving from deterministic nonlinear dynamical systems. By treating such processes as deterministic, it is possible to uncover the complicated dynamics and make realistic short-term predictions. Such systems exhibit stable properties which are predictable at times, but become “chaotic” under certain initial conditions. Chaos theory describes the behavior of certain nonlinear dynamical systems that may exhibit dynamics that are highly sensitive to initial conditions, popularly referred to as the “butterfly effect”. The first step in the analysis of chaotic dynamics is to establish whether a time series is in fact generated from a chaotic deterministic system, which is done by estimating certain invariant measures such as the correlation dimension, Lyapunov exponent, Kolmogorov–Sinai (KS) entropy, capacity dimension, topological dimension, fractal dimension, Hausdorff dimension, etc. The methods of their estimation are varied and have limitations. This Special Issue on Chaotic Dynamics of Environmental and Htydrological Time Series aims to bring together the latest developments in the understanding of chaotic dynamics with particular reference to the prediction of environmental time series.
Dr. Amithirigala Widhanelage Jayawardena
Guest Editor
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Keywords
- false nearest neighbor method
- embedding dimension
- singular value decomposition
- delay time
- Kolmogorov–Sinai entropy
- correlation dimension
- Lyapunov exponent
- phase-space reconstruction and prediction
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