Robust Data Assimilation in River Flow and Stage Estimation Based on Multiple Imputation Particle Filter

被引：4

作者：

Ismail, Zool Hilmi ^{[1
,2
]}

Jalaludi, Nor Anija ^{[2
,3
]}

机构：

[1] Univ Teknol Malaysia, Ctr Artificial Intelligence & Robot, Kuala Lumpur 54100, Malaysia

[2] Univ Teknol Malaysia, Malaysia Japan Int Inst Technol, Kuala Lumpur 54100, Malaysia

[3] Univ Tun Hussein Onn Malaysia, Fac Elect & Elect Engn, Batu Pahat 86400, Malaysia

来源：

IEEE ACCESS | 2019年 / 7卷

关键词：

Estimation; Rivers; Mathematical model; Noise measurement; Data assimilation; Particle filters; Data models; multi-imputation particle filter; hydrodynamics; nonlinear system; Kalman filter; ENSEMBLE KALMAN FILTER; NONLINEAR STATE ESTIMATION; STREAMFLOW; CONVERGENCE; PERFORMANCE;

D O I：

10.1109/ACCESS.2019.2949616

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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).

引用

页码：159226 / 159238

页数：13

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