Robust data assimilation in river flow and stage estimation based on multiple imputation particle filter
In this paper, new method is proposed for a more robust Data Assimilation (DA) design of the river flow and stage estimation. By using the new sets of data that are derived from the incorporated Multi Imputation Particle Filter (MIPF) in the DA structure, the proposed method is found to have overc...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2019
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| Subjects: | |
| Online Access: | http://eprints.uthm.edu.my/4045/ http://eprints.uthm.edu.my/4045/1/J11909_38ca1c621876c8a34ba9703cfeed20a1.pdf |
| Summary: | In this paper, new method is proposed for a more robust Data Assimilation (DA) design of the
river flow and stage estimation. By using the new sets of data that are derived from the incorporated Multi
Imputation Particle Filter (MIPF) in the DA structure, the proposed method is found to have overcome the
issue of missing observation data and contributed to a better estimation process. The convergence analysis
of the MIPF is discussed and shows that the number of the particles and imputation influence the ability of
this method to perform estimation. The simulation results of the MIPF demonstrated the superiority of the
proposed approach when being compared to the Extended Kalman Filter (EKF) and Particle Filter (PF). |
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