The ensemble particle filter (EnPF) in rainfall-runoff models

Delft, GA; Serafy, G.Y. El; Heemink, A.W.

doi:10.1007/s00477-008-0301-z

Cited by 32 publications

(15 citation statements)

References 11 publications

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“…Since the PF needs to resolve higher-order distribution properties that are ignored by the EnKF, more particles may be required in the implementation of the PFs for reliable comparisons with the EnKF. In a recent study, 500 particles were chosen for assimilation to achieve reliable comparisons between the PF and the EnKF for rainfall runoff [50]. Zhou et al [54] found that particle sizes greater than 800 resulted in nearly the same RMSEs between the PF and the EnKF for root zone soil moisture.…”

Section: Comparison Of Pf and Enkfmentioning

confidence: 98%

Particle Filter-based assimilation algorithms for improved estimation of root-zone soil moisture under dynamic vegetation conditions

Nagarajan

Judge

Graham

et al. 2011

Advances in Water Resources

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Section: Comparison Of Pf and Enkfmentioning

confidence: 98%

Particle Filter-based assimilation algorithms for improved estimation of root-zone soil moisture under dynamic vegetation conditions

Nagarajan

Judge

Graham

et al. 2011

Advances in Water Resources

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“…Weerts and El Serafy (2006) compared the capabilities of the EnKF with the particle filter at improving flood forecast predictions using the quasi-distributed HBV-96 model and reported that the EnKF outperformed the particle filter at low flow conditions and when the ensemble size was small. Using the HyMOD, Van Delft et al (2009) proposed an approach for combining ensemble Kalman filter and particle filter algorithms. In their analysis they relied on small ensemble sizes (\150) which was the basis in their conclusion about the performance of each procedure neglecting the fact that the particle filter can provide better result when more particles are used (Moradkhani et al 2005b;Zhou et al 2006).…”

Section: Sequential Bayesian Estimation Using Monte Carlo Simulationmentioning

confidence: 99%

Snow water equivalent prediction using Bayesian data assimilation methods

Leisenring

Moradkhani

2010

Stoch Environ Res Risk Assess

120

128

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Using the U.S. National Weather Service's SNOW-17 model, this study compares common sequential data assimilation methods, the ensemble Kalman filter (EnKF), the ensemble square root filter (EnSRF), and four variants of the particle filter (PF), to predict seasonal snow water equivalent (SWE) within a small watershed near Lake Tahoe, California. In addition to SWE estimation, the various data assimilation methods are used to estimate five of the most sensitive parameters of SNOW-17 by allowing them to evolve with the dynamical system. Unlike Kalman filters, particle filters do not require Gaussian assumptions for the posterior distribution of the state variables. However, the likelihood function used to scale particle weights is often assumed to be Gaussian. This study evaluates the use of an empirical cumulative distribution function (ECDF) based on the Kaplan-Meier survival probability method to compute particle weights. These weights are then used in different particle filter resampling schemes. Detailed analyses are conducted for synthetic and real data assimilation and an assessment of the procedures is made. The results suggest that the particle filter, especially the empirical likelihood variant, is superior to the ensemble Kalman filter based methods for predicting model states, as well as model parameters.

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“…This calls for online estimation (also known as filtering), i.e. computations of estimates for the unknown or unmeasurable states of a time-varying system simultaneously as measurements for the system are obtained [4][5][6][7]. However, some state variables in wastewater treatment processes cannot be directly measured online (e.g.…”

Section: Introductionmentioning

confidence: 98%

Online nonlinear sequential Bayesian estimation of a biological wastewater treatment process

Lee

Hong

Suh

et al. 2011

Bioprocess Biosyst Eng

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Online estimation of unknown state variables is a key component in the accurate modelling of biological wastewater treatment processes due to a lack of reliable online measurement systems. The extended Kalman filter (EKF) algorithm has been widely applied for wastewater treatment processes. However, the series approximations in the EKF algorithm are not valid, because biological wastewater treatment processes are highly nonlinear with a time-varying characteristic. This work proposes an alternative online estimation approach using the sequential Monte Carlo (SMC) methods for recursive online state estimation of a biological sequencing batch reactor for wastewater treatment. SMC is an algorithm that makes it possible to recursively construct the posterior probability density of the state variables, with respect to all available measurements, through a random exploration of the states by entities called 'particle'. In this work, the simplified and modified Activated Sludge Model No. 3 with nonlinear biological kinetic models is used as a process model and formulated in a dynamic state-space model applied to the SMC method. The performance of the SMC method for online state estimation applied to a biological sequencing batch reactor with online and offline measured data is encouraging. The results indicate that the SMC method could emerge as a powerful tool for solving online state and parameter estimation problems without any model linearization or restrictive assumptions pertaining to the type of nonlinear models for biological wastewater treatment processes.

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The ensemble particle filter (EnPF) in rainfall-runoff models

Cited by 32 publications

References 11 publications

Particle Filter-based assimilation algorithms for improved estimation of root-zone soil moisture under dynamic vegetation conditions

Particle Filter-based assimilation algorithms for improved estimation of root-zone soil moisture under dynamic vegetation conditions

Snow water equivalent prediction using Bayesian data assimilation methods

Online nonlinear sequential Bayesian estimation of a biological wastewater treatment process

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