Efficient data assimilation method based on chaos theory and Kalman filter with an application in Singapore Regional Model

Journal of Hydro-Environment Research

Volumn 3, Issue 2, 2009, Pages 85-95

  Sun, Yabin ^a   Sisomphon, Piyamarn ^b   Babovic, Vladan ^a   Chan, Eng Soon ^a  

^a NATIONAL UNIVERSITY OF SINGAPORE   (Singapore)

^b DELTARES   (Netherlands)

Author keywords

Chaos theory; Data assimilation; Kalman filter; Local model; Singapore Regional Model

Indexed keywords

BASED-ON CHAOS; COMPUTATIONAL DOMAINS; DATA ASSIMILATION; DATA ASSIMILATION METHODS; DELFT3D; ERROR DISTRIBUTIONS; ERROR PREDICTION; HYBRID SCHEME; INITIAL CONDITIONS; LIMITED INFORMATION; LOCAL MODEL; MODEL ERRORS; MODEL OUTPUTS; NUMERICAL MODELS; OBSERVED DATA; PREDICTED ERROR; PROPAGATION OF ERROR; REGIONAL MODEL; SINGAPORE; SINGAPORE REGIONAL MODEL; SPATIAL ERRORS; TEMPORAL ERRORS; TIME HORIZONS;

ALGORITHMS; COMPUTATIONAL EFFICIENCY; CONTROL THEORY; DATA PROCESSING; KALMAN FILTERS; MEASUREMENT ERRORS; PACKET NETWORKS;

CHAOS THEORY;

ALGORITHM; CHAOS THEORY; DATA ASSIMILATION; ERROR ANALYSIS; KALMAN FILTER;

ASIA; EURASIA; SINGAPORE [SOUTHEAST ASIA]; SOUTHEAST ASIA;

EID: 69949115057     PISSN: 15706443     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jher.2009.07.003     Document Type: Article

References (26)

1. Abarbanel H.D.I. Analysis of Observed Chaotic Data (1998), Springer-Verlag, New York
2. Babovic V., Canizares R., Jensen H.R., and Klinting A. Neural networks as routine for error updating of numerical models. J. Hydraul. Eng. 127 3 (2001) 181-193
3. Babovic, V., Keijzer, M., Bundzel, M., 2000a. From global to local modelling: a case study in error correction of deterministic models. In: Proceedings fourth International Conference on Hydroinformatics, Iowa city.
4. Babovic V., and Keijzer M. Forecasting of river discharges in the presence of chaos and noise. In: Marsalek J. (Ed). Coping with Floods: Lessons Learned for Recent Experiences (1999), Kluwer, Dordrecht 405-420
5. Babovic, V., Keijzer, M., Stefannson, M., 2000b. Optimal embedding using evolutionary algorithms. In: Proceedings fourth International Conference on Hydroinformatics, Iowa city.
6. Babovic V., Sannasiraj S.A., and Chan E.S. Error correction of a predictive ocean wave model using local model approximation. J. Mar. Sys. 53 1 (2005) 1-17
7. Cañizares, R., 1999. On the application of data assimilation in regional coastal models. Ph.D. Thesis, TU Delft, Rotterdam.
8. Cañizares R., Madsen H., Jensen H.R., and Vested H.J. Developments in operational shelf sea modelling in Danish waters. Estuar. Coast. Shelf Sci. 53 4 (2001) 595-605
9. Daley R. Atmospheric Data Analysis (1991), Cambridge Univ. Press, Cambridge
10. Evensen G. Sequential data assimilation with nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics. J. Geophys. Res. 99 C5 (1994) 10143-10162
11. Farmer J.D., and Sidorowich J.J. Predicting chaotic time series. Phys. Rev. Lett. 59 8 (1987) 845-848
12. Goldberg D.E. Genetic Algorithms in Search, Optimization and Machine Learning (1989), Addison-Wesley Publishing Company, Boston
13. Hayking S. Kalman Filtering and Neural Networks (2001), Wiley & Sons, New York
14. Hydrographic Department of Singapore, 1999. Singapore Tide Tables and Port Information. ISSN:0219-0282, Singapore.
15. Kalman R.E. A new approach to linear filtering and prediction theory. J. Basic Eng. 82D (1960) 35-45
16. Keijzer M., and Babovic V. Error correction of a deterministic model in Venice lagoon by local linear models. Proc. Modelli Complessi e Metodi Computazionali Intensivi per la Stima e la Previsione (1999) Venice
17. Kernkamp H., and Zijl F. Further Hydraulic Model Studies for Pulau Ubin & Pulau Tekong Reclamation Scheme. Interim Report on Hydrodynamic Modelling - Model Set-up and Calibration (2004), Delft Hydraulics, Delft, The Netherlands
18. Madsen H., and Cañizares R. Comparison of extended and ensemble Kalman filters for data assimilation in coastal area modelling. Int. J. Numer. Methods Fluids 31 6 (1999) 961-981
19. Ott E. Chaos in Dynamical Systems (1993), Cambridge Univ. Press, Cambridge
20. Phoon K.K., Islam M.N., Liaw C.Y., and Liong S.Y. A practical inverse approach for forecasting nonlinear hydrological time series. J. Hydrol. Eng. 7 2 (2002) 116-128
21. Sørensen, J.V.T., Madsen, H., Madsen, H., 2001. Data assimilation of tidal gauge data in a three-dimensional coastal model. In: Proceedings of the fourth DHI Software Conference, Helsingør.
22. Sprott J.C. Chaos and Time-Series Analysis (2003), Oxford Univ. Press, Oxford
23. Sun Y.B., Sisomphon P., Babovic V., and Chan E.S. Applying local model approach for tidal prediction in a deterministic model. Int. J. Numer. Methods Fluids (2008) 10.1002/fld.1910 Published online in Wiley InterScience
24. Takens F. Detecting strange attractors in turbulence. In: Rand D.A., and Yong L.S. (Eds). Dynamical Systems and Turbulence, Lecture Notes in Mathematics vol. 898 (1981), Springer-Verlag, Berlin 366-381
25. Welch G., and Bishop G. An Introduction to the Kalman Filter. Technical Report (2001), Dept. of Computer Science, Univ. of North Carolina, Chapel Hill
26. Williams G.P. Chaos Theory Tamed (1997), Taylor & Francis, London