Premium
CHAOTIC FORECASTING OF DISCHARGE TIME SERIES: A CASE STUDY 1
Author(s) -
Lisi Francesco,
Villi Vigilio
Publication year - 2001
Publication title -
jawra journal of the american water resources association
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.957
H-Index - 105
eISSN - 1752-1688
pISSN - 1093-474X
DOI - 10.1111/j.1752-1688.2001.tb00967.x
Subject(s) - chaotic , lyapunov exponent , correlation dimension , attractor , phase portrait , series (stratigraphy) , nonlinear system , statistical physics , mathematics , time series , computer science , dimension (graph theory) , set (abstract data type) , statistics , fractal dimension , physics , mathematical analysis , bifurcation , artificial intelligence , fractal , geology , pure mathematics , paleontology , quantum mechanics , programming language
This paper considers the problem of forecasting the discharge time series of a river by means of a chaotic approach. To this aim, we first check for some evidence of chaotic behavior in the dynamic by considering a set of different procedures, namely, the phase portrait of the attractor, the correlation dimension, and the largest Lyapunov exponent. Their joint application seems to confirm the presence of a nonlinear deterministic dynamic of chaotic type. Second, we consider the so‐called nearest neighbors predictor and we compare it with a classical linear model. By comparing these two predictors, it seems that nonlinear river flow modeling, and in particular chaotic modeling, is an effective method to improve predictions.