CASPIAN SEA LEVEL PREDICTION USING ARTIFICIAL NEURAL NETWORK AND EMPIRICAL MODE DECOMPOSITION

Abstract

This paper demonstrates the possibility of using nonlinear modeling for prediction of the Caspian Sea level. Phase space geometry of the of a model can be reconstructed by the embedology methods. Dynamical invariants, such as the Lyapunov exponents, the Kaplan-Yorke dimension, and the prediction horizon were estimated from reconstruction. Fractal and multifractal analyses were carried out for various time intervals of the Caspian Sea level and multifractal spectra were calculated. Then, historical data resolution was improved with the help of fractal approximation. The EMD method was used to reduce noise of the time series. Global nonlinear predictions were made with the help of Artificial Neural Network for combinations of different empirical modes.

Keywords

• caspian sea level
• fractal approximation
• multifractal analysis
• empirical mode decomposition
• embedology
• nonlinear prediction