EC-SVM approach for real-time hydrologic forecasting

  title={EC-SVM approach for real-time hydrologic forecasting},
  author={Xinying Yu and Shie-Yui Liong and Vladan Babovic},
This study demonstrates a combined application of chaos theory and support vector machine (SVM) in the analysis of chaotic time series with a very large sample data record. A large data record is often required and causes computational difficulty. The decomposition method is used in this study to circumvent this difficulty. The various parameters inherent in chaos technique and SVM are optimised, with the assistance of an evolutionary algorithm, to yield the minimal prediction error. The… CONTINUE READING
Highly Cited
This paper has 46 citations. REVIEW CITATIONS
27 Citations
28 References
Similar Papers


Publications citing this paper.
Showing 1-10 of 27 extracted citations


Publications referenced by this paper.
Showing 1-10 of 28 references

A robust and efficient scheme in search for optimal prediction parameters set in chaotic time series

  • S. Y. Liong, K. K. Phoon, M.F.K. Pasha, C. D. Doan
  • First Asia Pacific DHI Software Conference…
  • 2002
Highly Influential
11 Excerpts

Practical inverse approach for forecasting nonlinear hydrological time series

  • K. K. Phoon, M. N. Islam, C. Y. Liaw, S. Y. Liong
  • J. Hydrol. Engng. ASCE
  • 2002
Highly Influential
10 Excerpts

Analysis of Observed Chaotic Data

  • H.D.I. Abarbanel
  • 1996
Highly Influential
11 Excerpts

Flood stage forecasting with SVM

  • S. Y. Liong, C. Sivapragasm
  • J. Am. Wat. Res. Assoc
  • 2002
1 Excerpt

2001Multivariate nonlinear prediction of river flows

  • A. Porporato, L. Ridolfi
  • J. Hydrol
  • 2001
1 Excerpt

Multivariate nonlinear prediction of river flows

  • B. Schölkopf, C. Burges, A. Smola, L. Ridolfi
  • J . Hydrol .
  • 2001

Similar Papers

Loading similar papers…