Monthly runoff prediction using phase-space reconstruction
(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)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/e17e8e60-7da6-4960-b292-61881f7b00e7
- author
- Sivakumar, Bellie ; Berndtsson, Ronny LU and Persson, Magnus LU
- organization
- publishing date
- 2001
- 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
- 2022-10-08 06:00:11
@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}}, keywords = {{runoff; prediction; chaotic dynamics; phphase space reconstruction; local approximation; Coaracy Nunes/Araguari River basin; Brazil}}, 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}}, }