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Monthly runoff prediction using phase-space reconstruction

Sivakumar, Bellie ; Berndtsson, Ronny LU and Persson, Magnus LU (2001) In Hydrological Sciences Journal 46(3). p.377-387
Abstract
A nonlinear prediction method, developed based on the ideas gained from deterministic chaos theory, is employed: (a) to predict monthly runoff; and (b) to detect the possible presence of chaos in runoff dynamics. The method first reconstructs the single-dimensional (or variable) runoff series in a multi-dimensional phase space to represent its dynamics, and then uses a local polynomial approach to make predictions. Monthly runoff series observed at the Coaracy Nunes/Araguari River basin in northern Brazil is studied. The predictions are found to be in close agreement with the observed runoff, with high correlation coefficient and coefficient of efficiency values, indicating the suitability of the nonlinear prediction method for predicting... (More)
A nonlinear prediction method, developed based on the ideas gained from deterministic chaos theory, is employed: (a) to predict monthly runoff; and (b) to detect the possible presence of chaos in runoff dynamics. The method first reconstructs the single-dimensional (or variable) runoff series in a multi-dimensional phase space to represent its dynamics, and then uses a local polynomial approach to make predictions. Monthly runoff series observed at the Coaracy Nunes/Araguari River basin in northern Brazil is studied. The predictions are found to be in close agreement with the observed runoff, with high correlation coefficient and coefficient of efficiency values, indicating the suitability of the nonlinear prediction method for predicting the runoff dynamics. The results also reveal the presence of low-dimensional chaos in the runoff dynamics, when an inverse approach is adopted for identification, as: (a) an optimal embedding dimension exists, and (b) the prediction accuracy decreases with an increase in prediction lead lime. (Less)
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author
; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
runoff, prediction, chaotic dynamics, phphase space reconstruction, local approximation, Coaracy Nunes/Araguari River basin, Brazil
in
Hydrological Sciences Journal
volume
46
issue
3
pages
11 pages
publisher
Taylor & Francis
external identifiers
  • scopus:0035369390
  • wos:000169275500004
ISSN
0262-6667
DOI
10.1080/02626660109492833
language
English
LU publication?
yes
id
e17e8e60-7da6-4960-b292-61881f7b00e7
date added to LUP
2018-05-30 14:10:59
date last changed
2020-12-08 03:27:37
@article{e17e8e60-7da6-4960-b292-61881f7b00e7,
  abstract     = {A nonlinear prediction method, developed based on the ideas gained from deterministic chaos theory, is employed: (a) to predict monthly runoff; and (b) to detect the possible presence of chaos in runoff dynamics. The method first reconstructs the single-dimensional (or variable) runoff series in a multi-dimensional phase space to represent its dynamics, and then uses a local polynomial approach to make predictions. Monthly runoff series observed at the Coaracy Nunes/Araguari River basin in northern Brazil is studied. The predictions are found to be in close agreement with the observed runoff, with high correlation coefficient and coefficient of efficiency values, indicating the suitability of the nonlinear prediction method for predicting the runoff dynamics. The results also reveal the presence of low-dimensional chaos in the runoff dynamics, when an inverse approach is adopted for identification, as: (a) an optimal embedding dimension exists, and (b) the prediction accuracy decreases with an increase in prediction lead lime.},
  author       = {Sivakumar, Bellie and Berndtsson, Ronny and Persson, Magnus},
  issn         = {0262-6667},
  language     = {eng},
  number       = {3},
  pages        = {377--387},
  publisher    = {Taylor & Francis},
  series       = {Hydrological Sciences Journal},
  title        = {Monthly runoff prediction using phase-space reconstruction},
  url          = {http://dx.doi.org/10.1080/02626660109492833},
  doi          = {10.1080/02626660109492833},
  volume       = {46},
  year         = {2001},
}