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Dual state-parameter estimation of hydrological models using ensemble Kalman filter
Hydrologic models are twofold: models for understanding physical processes and models for prediction. This study addresses the latter, which modelers use to predict, for example, streamflow at some future time given knowledge of the current state of the system and model parameters. In this respect, good estimates of the parameters and state variables are needed to enable the model to generate accurate forecasts. In this paper, a dual state-parameter estimation approach is presented based on the Ensemble Kalman Filter (EnKF) for sequential estimation of both parameters and state variables of a hydrologic model. A systematic approach for identification of the perturbation factors used for ensemble generation and for selection of ensemble size is discussed. The dual EnKF methodology introduces a number of novel features: (1) both model states and parameters can be estimated simultaneously; (2) the algorithm is recursive and therefore does not require storage of all past information, as is the case in the batch calibration procedures; and (3) the various sources of uncertainties can be properly addressed, including input, output, and parameter uncertainties. The applicability and usefulness of the dual EnKF approach for ensemble streamflow forecasting is demonstrated using a conceptual rainfall-runoff model. © 2004 Elsevier Ltd. All rights reserved
The ROMES method for statistical modeling of reduced-order-model error
This work presents a technique for statistically modeling errors introduced
by reduced-order models. The method employs Gaussian-process regression to
construct a mapping from a small number of computationally inexpensive `error
indicators' to a distribution over the true error. The variance of this
distribution can be interpreted as the (epistemic) uncertainty introduced by
the reduced-order model. To model normed errors, the method employs existing
rigorous error bounds and residual norms as indicators; numerical experiments
show that the method leads to a near-optimal expected effectivity in contrast
to typical error bounds. To model errors in general outputs, the method uses
dual-weighted residuals---which are amenable to uncertainty control---as
indicators. Experiments illustrate that correcting the reduced-order-model
output with this surrogate can improve prediction accuracy by an order of
magnitude; this contrasts with existing `multifidelity correction' approaches,
which often fail for reduced-order models and suffer from the curse of
dimensionality. The proposed error surrogates also lead to a notion of
`probabilistic rigor', i.e., the surrogate bounds the error with specified
probability
A Bayesian Consistent Dual Ensemble Kalman Filter for State-Parameter Estimation in Subsurface Hydrology
Ensemble Kalman filtering (EnKF) is an efficient approach to addressing
uncertainties in subsurface groundwater models. The EnKF sequentially
integrates field data into simulation models to obtain a better
characterization of the model's state and parameters. These are generally
estimated following joint and dual filtering strategies, in which, at each
assimilation cycle, a forecast step by the model is followed by an update step
with incoming observations. The Joint-EnKF directly updates the augmented
state-parameter vector while the Dual-EnKF employs two separate filters, first
estimating the parameters and then estimating the state based on the updated
parameters. In this paper, we reverse the order of the forecast-update steps
following the one-step-ahead (OSA) smoothing formulation of the Bayesian
filtering problem, based on which we propose a new dual EnKF scheme, the
Dual-EnKF. Compared to the Dual-EnKF, this introduces a new update
step to the state in a fully consistent Bayesian framework, which is shown to
enhance the performance of the dual filtering approach without any significant
increase in the computational cost. Numerical experiments are conducted with a
two-dimensional synthetic groundwater aquifer model to assess the performance
and robustness of the proposed Dual-EnKF, and to evaluate its
results against those of the Joint- and Dual-EnKFs. The proposed scheme is able
to successfully recover both the hydraulic head and the aquifer conductivity,
further providing reliable estimates of their uncertainties. Compared with the
standard Joint- and Dual-EnKFs, the proposed scheme is found more robust to
different assimilation settings, such as the spatial and temporal distribution
of the observations, and the level of noise in the data. Based on our
experimental setups, it yields up to 25% more accurate state and parameters
estimates
Extremum Seeking-based Iterative Learning Linear MPC
In this work we study the problem of adaptive MPC for linear time-invariant
uncertain models. We assume linear models with parametric uncertainties, and
propose an iterative multi-variable extremum seeking (MES)-based learning MPC
algorithm to learn on-line the uncertain parameters and update the MPC model.
We show the effectiveness of this algorithm on a DC servo motor control
example.Comment: To appear at the IEEE MSC 201
Innate and discretionary accruals quality and corporate governance
This paper extends previous research on the association between corporate governance mechanisms and accruals quality. We derive measures of the discretionary and innate components of accruals quality and regress them against corporate governance characteristics. For discretionary accruals, we find use of a Big 4 audit firm and a larger audit committee as the primary governance mechanisms associated with higher accruals quality. For innate accruals quality, we find that higher quality is associated with an independent board of directors, a larger, more independent and more active audit committee, and use of a Big 4 audit firm. Our findings suggest a stronger relation between sound governance mechanisms and innate accruals quality than discretionary accruals quality.Full Tex
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