Sequential ensemble-based optimal design for parameter estimation
Abstract
The ensemble Kalman filter (EnKF) has been widely used in parameter estimation for hydrological models. The focus of most previous studies was to develop more efficient analysis (estimation) algorithms. On the other hand, it is intuitively understandable that a well-designed sampling (data-collection) strategy should provide more informative measurements and subsequently improve the parameter estimation. In this work, a Sequential Ensemble-based Optimal Design (SEOD) method, coupled with EnKF, information theory and sequential optimal design, is proposed to improve the performance of parameter estimation. Based on the first-order and second-order statistics, different information metrics including the Shannon entropy difference (SD), degrees of freedom for signal (DFS) and relative entropy (RE) are used to design the optimal sampling strategy, respectively. The effectiveness of the proposed method is illustrated by synthetic one-dimensional and two-dimensional unsaturated flow case studies. It is shown that the designed sampling strategies can provide more accurate parameter estimation and state prediction compared with conventional sampling strategies. Optimal sampling designs based on various information metrics perform similarly in our cases. Furthermore, the effect of ensemble size on the optimal design is also investigated. Overall, larger ensemble size improves the parameter estimation and convergence of optimal sampling strategy. Although the proposed methodmore »
- Authors:
-
- Zhejiang Univ., Hangzhou (China)
- Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
- Univ. of California, Riverside, CA (United States)
- Publication Date:
- Research Org.:
- Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
- Sponsoring Org.:
- National Natural Science Foundation of China (NSFC); Fundamental Research Funds for the Central Universities (China); USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR)
- OSTI Identifier:
- 1356516
- Report Number(s):
- PNNL-SA-123579
Journal ID: ISSN 0043-1397; KJ0401000
- Grant/Contract Number:
- AC05-76RL01830; 41371237; 41571215; 2016QNA6008
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Water Resources Research
- Additional Journal Information:
- Journal Volume: 52; Journal Issue: 10; Journal ID: ISSN 0043-1397
- Publisher:
- American Geophysical Union (AGU)
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 54 ENVIRONMENTAL SCIENCES; ensemble Kalman filter; experimental design; parameter estimation; unsaturated flow
Citation Formats
Man, Jun, Zhang, Jiangjiang, Li, Weixuan, Zeng, Lingzao, and Wu, Laosheng. Sequential ensemble-based optimal design for parameter estimation. United States: N. p., 2016.
Web. doi:10.1002/2016WR018736.
Man, Jun, Zhang, Jiangjiang, Li, Weixuan, Zeng, Lingzao, & Wu, Laosheng. Sequential ensemble-based optimal design for parameter estimation. United States. https://doi.org/10.1002/2016WR018736
Man, Jun, Zhang, Jiangjiang, Li, Weixuan, Zeng, Lingzao, and Wu, Laosheng. Mon .
"Sequential ensemble-based optimal design for parameter estimation". United States. https://doi.org/10.1002/2016WR018736. https://www.osti.gov/servlets/purl/1356516.
@article{osti_1356516,
title = {Sequential ensemble-based optimal design for parameter estimation},
author = {Man, Jun and Zhang, Jiangjiang and Li, Weixuan and Zeng, Lingzao and Wu, Laosheng},
abstractNote = {The ensemble Kalman filter (EnKF) has been widely used in parameter estimation for hydrological models. The focus of most previous studies was to develop more efficient analysis (estimation) algorithms. On the other hand, it is intuitively understandable that a well-designed sampling (data-collection) strategy should provide more informative measurements and subsequently improve the parameter estimation. In this work, a Sequential Ensemble-based Optimal Design (SEOD) method, coupled with EnKF, information theory and sequential optimal design, is proposed to improve the performance of parameter estimation. Based on the first-order and second-order statistics, different information metrics including the Shannon entropy difference (SD), degrees of freedom for signal (DFS) and relative entropy (RE) are used to design the optimal sampling strategy, respectively. The effectiveness of the proposed method is illustrated by synthetic one-dimensional and two-dimensional unsaturated flow case studies. It is shown that the designed sampling strategies can provide more accurate parameter estimation and state prediction compared with conventional sampling strategies. Optimal sampling designs based on various information metrics perform similarly in our cases. Furthermore, the effect of ensemble size on the optimal design is also investigated. Overall, larger ensemble size improves the parameter estimation and convergence of optimal sampling strategy. Although the proposed method is applied to unsaturated flow problems in this study, it can be equally applied in any other hydrological problems.},
doi = {10.1002/2016WR018736},
journal = {Water Resources Research},
number = 10,
volume = 52,
place = {United States},
year = {Mon Oct 03 00:00:00 EDT 2016},
month = {Mon Oct 03 00:00:00 EDT 2016}
}
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