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...

Full description

Bibliographic Details
Main Authors: Ismail, Zool Hilmi, Jalaludin, Nor Anija
Format: Article
Language:English
Published: IEEE 2019
Subjects:
Online Access:http://eprints.uthm.edu.my/4045/
http://eprints.uthm.edu.my/4045/1/J11909_38ca1c621876c8a34ba9703cfeed20a1.pdf
_version_ 1848888181756264448
author Ismail, Zool Hilmi
Jalaludin, Nor Anija
author_facet Ismail, Zool Hilmi
Jalaludin, Nor Anija
author_sort Ismail, Zool Hilmi
building UTHM Institutional Repository
collection Online Access
description 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).
first_indexed 2025-11-15T20:06:12Z
format Article
id uthm-4045
institution Universiti Tun Hussein Onn Malaysia
institution_category Local University
language English
last_indexed 2025-11-15T20:06:12Z
publishDate 2019
publisher IEEE
recordtype eprints
repository_type Digital Repository
spelling uthm-40452021-11-24T01:17:38Z http://eprints.uthm.edu.my/4045/ Robust data assimilation in river flow and stage estimation based on multiple imputation particle filter Ismail, Zool Hilmi Jalaludin, Nor Anija QA273-280 Probabilities. Mathematical statistics 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). IEEE 2019 Article PeerReviewed text en http://eprints.uthm.edu.my/4045/1/J11909_38ca1c621876c8a34ba9703cfeed20a1.pdf Ismail, Zool Hilmi and Jalaludin, Nor Anija (2019) Robust data assimilation in river flow and stage estimation based on multiple imputation particle filter. IEEE Access, 7.
spellingShingle QA273-280 Probabilities. Mathematical statistics
Ismail, Zool Hilmi
Jalaludin, Nor Anija
Robust data assimilation in river flow and stage estimation based on multiple imputation particle filter
title Robust data assimilation in river flow and stage estimation based on multiple imputation particle filter
title_full Robust data assimilation in river flow and stage estimation based on multiple imputation particle filter
title_fullStr Robust data assimilation in river flow and stage estimation based on multiple imputation particle filter
title_full_unstemmed Robust data assimilation in river flow and stage estimation based on multiple imputation particle filter
title_short Robust data assimilation in river flow and stage estimation based on multiple imputation particle filter
title_sort robust data assimilation in river flow and stage estimation based on multiple imputation particle filter
topic QA273-280 Probabilities. Mathematical statistics
url http://eprints.uthm.edu.my/4045/
http://eprints.uthm.edu.my/4045/1/J11909_38ca1c621876c8a34ba9703cfeed20a1.pdf