River Flow and Stage Estimation with Missing Observation Data using Multi Imputation Particle Filter (MIPF) Method
Keywords:
Missing Data, State Estimation, Multi Imputation Particle FilterAbstract
An advanced knowledge of the river condition helps for better source management. This information can be gathered via estimation using DA methods. The DA methods blend the system model with the observation data to obtain the estimated river flow and stage. However, the observation data may contain some missing data due to the hardware power limitations, unreliable channel, sensor failure and etc. This problem limits the ability of the standard method such as EKF, EnKF and PF. The Multi Imputation Particle Filter (MIPF) able to deal with this problem since it allows for new input data to replace the missing data. The result shows that the performance of the river flow and stage estimation is depending on the number of particles and imputation used. The performance is evaluated by comparing the estimated velocity obtained using the estimated flow and stage, with the measured velocity. The result shows that higher number of particles and imputation ensure better estimation result.References
A. Y. Sun, D. Wang, and X. Xu, “Monthly streamflow forecasting using Gaussian Process Regression,” Journal of Hydrology., vol. 511, pp. 72–81, Apr. 2014.
A. Tinka, M. Rafiee, and A. M. Bayen, “Floating sensor networks for river studies,” IEEE System Journal, vol. 7, no. 1, pp. 36–49, 2013.
S. Michelin and O. Doaré, “Energy harvesting efficiency of piezoelectric flags in axial flows,” Journal of Fluid Mechanics., vol. 714, pp. 489–504, Jan. 2013.
P. J. Smith, G. D. Thornhill, S. L. Dance, A. S. Lawless, D. C. Mason, and N. K. Nichols, “Data assimilation for state and parameter estimation: Application to morphodynamic modelling,” Quarterly Journal of Royal Meteorological Society, vol. 139, no. 671, pp. 314–327, 2013.
Y. Liu and H. V. Gupta, “Uncertainty in hydrologic modeling: Toward an integrated data assimilation framework,” Water Resource Research, vol. 43, pp. 1–18, 2007.
S. A. Gadsden, M. Al-Shabi, I. Arasaratnam, and S. R. Habibi, “Combined cubature Kalman and smooth variable structure filtering: A robust nonlinear estimation strategy,” Signal Processing, vol. 96, no. PART B, pp. 290–299, 2014.
X. He, R. Sithiravel, R. Tharmarasa, B. Balaji, and T. Kirubarajan, “A spline filter for multidimensional nonlinear state estimation,” Signal Processing, vol. 102, pp. 282–295, 2014.
S. Kim, D.-J. Seo, H. Riazi, and C. Shin, “Improving water quality forecasting via data assimilation – Application of maximum likelihood ensemble filter to HSPF,” Journal of Hydrology, Oct. 2014.
L. Bertino, G. Evensen, and H. Wackernagel, “Sequential Data Assimilation Techniques in Oceanography,” International Statistical Review, vol. 71, no. 2, pp. 223–241, 2003.
J. Samuel, P. Coulibaly, G. Dumedah, and H. Moradkhani, “Assessing model state and forecasts variation in hydrologic data assimilation,” Journal of Hydrology, vol. 513, pp. 127–141, May 2014.
G. G. Rigatos, “A derivative-free kalman filtering approach to state estimation-based control of nonlinear systems,” IEEE Transaction on Industrial Electronics, vol. 59, no. 10, pp. 3987–3997, 2012.
H. Chen, D. Yang, Y. Hong, J. J. Gourley, and Y. Zhang, “Hydrological data assimilation with the Ensemble Square-Root-Filter: Use of streamflow observations to update model states for real-time flash flood forecasting,” Advances in Water Resource, vol. 59, pp. 209–220, Sep. 2013.
T.-J. Chang, H.-M. Kao, K.-H. Chang, and M.-H. Hsu, “Numerical simulation of shallow-water dam break flows in open channels using smoothed particle hydrodynamics,” Journal of Hydrology, vol. 408, pp. 78–90, Sep. 2011.
Y. Yuan, J. W. C. Van Lint, R. E. Wilson, F. Van Wageningen-kessels, and S. P. Hoogendoorn, “Real-Time Lagrangian Traffic State Estimator for Freeways,” IEEE Transaction on Intelligent Transportation Systems, vol. 13, no. 1, pp. 59–70, 2012.
X. Zhang, A. S. Khwaja, J. Luo, A. S. Housfater, and A. Anpalagan, “Convergence Analysis of Multiple Imputations Particle Filters for dealing with Missing Data in Nonlinear Problems,” IEEE Journal of Selected Topic in Signal Processing, vol. 9, no. 8, pp. 2567–2570, 2014.
X. Litrico and V. Fromion, “Modeling of Open Channel Flow,” in Modeling and Control of Hydrosystems, 1st ed., Springer-Verlag London, 2009, pp. 17–41.
X. Liu, A. Mohammadian, J. Angel, and I. Sedano, “Irrigation & Drainage Systems Engineering One Dimensional Numerical Simulation of Bed Changes in Irrigation Channels using Finite Volume Method,” Irrigation and Drainage Systems Engineering, vol. 1, no. 2, pp. 1–6, 2012.
D. Crisan and A. Doucet, “A survey of convergence results on particle filtering methods for practitioners,” IEEE Transaction on Signal Processing, vol. 50, no. 3, pp. 736–746, 2002.
M. S. Arulampalam, S. Maskell, N. Gordon, and T. Clapp, “A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking,” IEEE Transaction on Signal Processing, vol. 50, no. 2, pp. 174–188, 2002.
X. Zhang, A. S. Khwaja, J. Luo, A. S. Housfater, and A. Anpalagan, “Multiple Imputations Particle Filters: Convergence and Performance Analyses for Nonlinear State Estimation With Missing Data,” IEEE Journal of Selected Topic in Signal Processing, vol. 9, no. 8, pp. 1536–1547, 2015.
Downloads
Published
How to Cite
Issue
Section
License
TRANSFER OF COPYRIGHT AGREEMENT
The manuscript is herewith submitted for publication in the Journal of Telecommunication, Electronic and Computer Engineering (JTEC). It has not been published before, and it is not under consideration for publication in any other journals. It contains no material that is scandalous, obscene, libelous or otherwise contrary to law. When the manuscript is accepted for publication, I, as the author, hereby agree to transfer to JTEC, all rights including those pertaining to electronic forms and transmissions, under existing copyright laws, except for the following, which the author(s) specifically retain(s):
- All proprietary right other than copyright, such as patent rights
- The right to make further copies of all or part of the published article for my use in classroom teaching
- The right to reuse all or part of this manuscript in a compilation of my own works or in a textbook of which I am the author; and
- The right to make copies of the published work for internal distribution within the institution that employs me
I agree that copies made under these circumstances will continue to carry the copyright notice that appears in the original published work. I agree to inform my co-authors, if any, of the above terms. I certify that I have obtained written permission for the use of text, tables, and/or illustrations from any copyrighted source(s), and I agree to supply such written permission(s) to JTEC upon request.