Welcome to International Network for Natural Sciences | INNSpub

Paper Details

Research Paper | July 1, 2014

| Download 2

Forecast the suspended load baron chay river by chaos theory

Ahmad Pour Mohammad Aghdam, Edris Merufinia, Hazhar Hadad

Key Words:

J. Bio. Env. Sci.5(1), 396-404, July 2014


JBES 2014 [Generate Certificate]


Estimation of rivers suspended load is one of the major issues of topics related to river engineering, reservoirs management, schemes and hydrologic projects. The chaotic behavior of monthly precipitation time series is investigated using the phase-space reconstruction technique and the principal component analysis method. To reconstruct phase space, the time delay and embedding dimension are needed and for this purpose, average mutual information and algorithm of false nearest neighbors are used. Correlation dimension method is applied for investigating chaotic behavior of the daily suspended sediment statistics, which is the resultant of correlation dimensions, expresses chaotic behavior in the time series to illustrate efficiency of chaos theory for predicting suspended sediment, daily suspended sediment statistics of Maku Baron Chay River investigated for 5years. The delay time and optimum embedding dimension were obtained 8 and 5 respectively. The low amount of correlation dimension (d = 3) represents the chaotic behavior of sediment time series.


Copyright © 2014
By Authors and International Network for
Natural Sciences (INNSPUB)
This article is published under the terms of the Creative
Commons Attribution Liscense 4.0

Forecast the suspended load baron chay river by chaos theory

Casdagli M. 1991. Chaos and deterministic versus stochastic non-linear modeling. J R Stat Soc, Ser B; 54(2), 303-28.

Elshorbagy A. Simonovic, S.P. &Panu, U.S. 2002.”.Noise reduction in chaotic hydrologic time series”.facts and doubts, Journal of Hydrology. 256, 147-165

Grassberger P, Procaccia I. 1983. Measuring the strangeness of strange attractors.Physica ;9, 189-208.

Ghorbani MA, KisiO, Aalinezhad M. 2011. A Probe into the Chaotic Nature of Daily Stream Flow time Series by Correlation Dimension and Largest Lyapanov Methods”, Applied Mathematical Modelling, 34, 4050-4057.

Farmer DJ, Sidorowich JJ. 1987. Predicting chaotic time serie. Phys Rev Lett; 59, 845-8.

Hense A. 1987. On the possible existence of a strange attractor for the southern oscillation. BeitrPhys Atmos; 60(1), 34-47.

Jayawardena AW, Lai F. 1994. Analysis and prediction of chaos in rainfall and stream flow time series. Journal of Hydrology. 153, 23-52.

Islam MN, Sivakumar B. 2002. Characterization and prediction of runoff dynamics: a nonlinear dynamical view. Advances in Water Resources. 25, 179-190.

Koçak K. 1997. Application of Local Prediction Model to Water Level Data. A satellite Conference to the 51st ISI Session in Istanbul, Turkey: Water and Statistics, Ankara-Turkey, pp: 185-193

Lisi F, Villi V. 2001. Chaotic forecasting of discharge time series: A case study, Journal of the American Water Resources Association. 37(2), 271- 279.

Ng WW, Panu US, Lennox WC. 2007. “Chaosbased Analytical techniques for daily extreme hydrological observations”, Journal ofHydrology, 342, 17–41.

Palus M. 1995. “Testing for nonlinearity using redundancies: Quantitative and qualitative aspects”. Physica, 80, 186-205.

Prichard D, Theiler J. 1995. Generalized redundancies for time series analysis. Physica; 84, 476-93.

Puente CE, Obregon N. 1996. A deterministic geometric representation of temporal rainfall: Results for a storm in Boston. Water Resources, 32(9), 2825-39.

Porporato A, Ridolfi L. 1997. Nonlinear analysis of river flow time sequences, Water Resources Research. 33(6), 1353-1367.

Qingfang M, Yuhua P. 2007. A new local linear prediction model for chaotic time series, Physics Letters A. 370, 465-470.

Regonda SK, Sivakumar B, Jain A. 2004. Temporal  scaling  in  river  flow:  can  it  bechaotic?, Hydrological Sciences Journal- desSciences Hydrologiques, 49(3), 373-385.

Stehlík J. 1999. Deterministic chaos in runoff series. Journal of Hydrology and Hydrodynamics. 47(4), 271-287.

Sangoyomi TB, Lall U, Abarbanel HD. 1996. Nonlinear dynamics of the Great Salt Lake: Dimension estimation. Water Resources; 32(1), 149-59.

Sivakumar B. 2000. Chaos theory in hydrology: Important issues and interpretations. Journal of Hydrology; 227, 1-20.

Takens F. 1980. Detecting strange attractors in turbulence, in Dynamical Systems and Turbulence, Lecture Notes in Mathematics, 898, edited by Rand DA, Young LS. Springer-Verlag, New York. 366-81.

Theiler J, Eubank S, Longtin A, Galdrikian B, Farmer JD. 1992.Testing for nonlinearity in time series: The method of surrogate data. Physics. 58, 77-94.

Wolf  A,  Swift  JB,  Swinney  HL,  Vastao  A. 1985.Determining Lyapunov exponents from d time series. Physics 16, 285-317.

Wilks DS. 1991. Representing serial correlation of meteorological events and forecasts in dynamic decision-analytic models. Monthly Weather Review 119, 1640-1662.


Style Switcher

Select Layout
Chose Color
Chose Pattren
Chose Background