Enhancing hydrologic data assimilation by evolutionary Particle Filter and Markov Chain Monte Carlo

Abbaszadeh, Peyman; Moradkhani, Hamid; Yan, Hongxiang

doi:10.1016/j.advwatres.2017.11.011

Cited by 106 publications

(113 citation statements)

References 33 publications

Supporting

Mentioning

108

Contrasting

Order By: Relevance

“…Therefore, the parameter setting of crossover and mutation probability has no significant impact on IEPFM as a result of MCMC simulation. This conclusion is consistent with that drawn by Abbaszadeh et al [23]. In this study, p c = 0.8 and p m = 0.3 are adopted, since RMSE resulting from this setting is slightly better.…”

Section: Sensitivity Analysissupporting

confidence: 91%

“…Recently, Qu et al established a machine learning model combining remote sensing data with experimental data to reveal the SM for the construction and management of sponge cities [22]. Abbaszadeh et al proposed a genetic evolutionary PF algorithm combining a genetic algorithm (GA) with MCMC to enhance hydrologic prediction [23]. Despite lots of attention being paid to AI algorithms in hydrologic DA, little research on intelligent PFs is inspired by the biological immune system.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Immune Evolution Particle Filter for Soil Moisture Data Assimilation

Sun

2019

Water

View full text Add to dashboard Cite

Data assimilation (DA) has been widely used in land surface models (LSM) to improve model state estimates. Among various DA methods, the particle filter (PF) with Markov chain Monte Carlo (MCMC) has become increasingly popular for estimating the states of the nonlinear and non-Gaussian LSMs. However, the standard PF always suffers from the particle impoverishment problem, characterized by loss of particle diversity. To solve this problem, an immune evolution particle filter with MCMC simulation inspired by the biological immune system, entitled IEPFM, is proposed for DA in this paper. The merit of this approach is in imitating the antibody diversity preservation mechanism to further improve particle diversity, thus increasing the accuracy of estimates. Furthermore, the immune memory function refers to promise particle evolution process towards optimal estimates. Effectiveness of the proposed approach is demonstrated by the numerical simulation experiment using a highly nonlinear atmospheric model. Finally, IEPFM is applied to a soil moisture (SM) assimilation experiment, which assimilates in situ observations into the Variable Infiltration Capacity (VIC) model to estimate SM in the MaQu network region of the Tibetan Plateau. Both synthetic and real case experiments demonstrate that IEPFM mitigates particle impoverishment and provides more accurate assimilation results compared with other popular DA algorithms.

show abstract

Section: Sensitivity Analysissupporting

confidence: 91%

Section: Introductionmentioning

confidence: 99%

Immune Evolution Particle Filter for Soil Moisture Data Assimilation

Sun

2019

Water

View full text Add to dashboard Cite

show abstract

“…This requires a larger ensemble size. Therefore, an adaptive factor similar to the EnKF (e.g., Wang and Bishop, 2003;Anderson, 2007;Bauser et al, 2018) is desirable for increasing the efficiency of the filter further and achieving a better uncertainty representation of the ensemble.…”

Section: Tuning Parameter γmentioning

confidence: 99%

“…While the MCMC is accurate, it is also expensive, as it requires additional model runs. To increase the efficiency, Abbaszadeh et al (2018) additionally combined it with a genetic algorithm.…”

Section: Introductionmentioning

confidence: 99%

Covariance resampling for particle filter – state and parameter estimation for soil hydrology

Berg

Bauser

Roth

2019

Hydrol. Earth Syst. Sci.

View full text Add to dashboard Cite

Particle filters are becoming increasingly popular for state and parameter estimation in hydrology. One of their crucial parts is the resampling after the assimilation step. We introduce a resampling method that uses the full weighted covariance information calculated from the ensemble to generate new particles and effectively avoid filter degeneracy. The ensemble covariance contains information between observed and unobserved dimensions and is used to fill the gaps between them. The covariance resampling approximately conserves the first two statistical moments and partly maintains the structure of the estimated distribution in the retained ensemble. The effectiveness of this method is demonstrated with a synthetic case -an unsaturated soil consisting of two homogeneous layers -by assimilating time-domain reflectometry-like (TDR-like) measurements. Using this approach we can estimate state and parameters for a rough initial guess with 100 particles. The estimated states and parameters are tested with a forecast after the assimilation, which is found to be in good agreement with the synthetic truth.Published by Copernicus Publications on behalf of the European Geosciences Union.

show abstract

“…Although, theoretically, these quantities can be estimated through hydrologic modeling, in practice they are often biased or erroneous due to the presence of uncertainties in all layers of hydrologic predictions. Data assimilation (DA) has been well received in the hydrologic community as one of the most effective methods in characterizing the aforementioned uncertainties while estimating parameters, prognostic, and diagnostic variables (Abbaszadeh et al, ; Clark et al, ; Moradkhani, Sorooshian, et al, ; Moradkhani et al, ; Pathiraja et al, ; Vrugt et al, ).…”

Section: Introductionmentioning

confidence: 99%

The Quest for Model Uncertainty Quantification: A Hybrid Ensemble and Variational Data Assimilation Framework

Abbaszadeh

Moradkhani

Daescu

2019

Water Resources Research

Self Cite

View full text Add to dashboard Cite

This article presents a novel approach to couple a deterministic four‐dimensional variational (4DVAR) assimilation method with the particle filter (PF) ensemble data assimilation system, to produce a robust approach for dual‐state‐parameter estimation. In our proposed method, the Hybrid Ensemble and Variational Data Assimilation framework for Environmental systems (HEAVEN), we characterize the model structural uncertainty in addition to model parameter and input uncertainties. The sequential PF is formulated within the 4DVAR system to design a computationally efficient feedback mechanism throughout the assimilation period. In this framework, the 4DVAR optimization produces the maximum a posteriori estimate of state variables at the beginning of the assimilation window without the need to develop the adjoint of the forecast model. The 4DVAR solution is then perturbed by a newly defined prior error covariance matrix to generate an initial condition ensemble for the PF system to provide more accurate and reliable posterior distributions within the same assimilation window. The prior error covariance matrix is updated from one cycle to another over the main assimilation period to account for model structural uncertainty resulting in an improved estimation of posterior distribution. The premise of the presented approach is that it (1) accounts for all sources of uncertainties involved in hydrologic predictions, (2) uses a small ensemble size, and (3) precludes the particle degeneracy and sample impoverishment. The proposed method is applied on a nonlinear hydrologic model and the effectiveness, robustness, and reliability of the method is demonstrated for several river basins across the United States.

show abstract

Enhancing hydrologic data assimilation by evolutionary Particle Filter and Markov Chain Monte Carlo

Cited by 106 publications

References 33 publications

Immune Evolution Particle Filter for Soil Moisture Data Assimilation

Immune Evolution Particle Filter for Soil Moisture Data Assimilation

Covariance resampling for particle filter – state and parameter estimation for soil hydrology

The Quest for Model Uncertainty Quantification: A Hybrid Ensemble and Variational Data Assimilation Framework

Contact Info

Product

Resources

About