A Bayesian approach to improved calibration and prediction of groundwater models with structural error
T Xu, AJ Valocchi - Water Resources Research, 2015 - Wiley Online Library
Numerical groundwater flow and solute transport models are usually subject to model
structural error due to simplification and/or misrepresentation of the real system, which
raises questions regarding the suitability of conventional least squares regression‐based
(LSR) calibration. We present a new framework that explicitly describes the model structural
error statistically in an inductive, data‐driven way. We adopt a fully Bayesian approach that
integrates Gaussian process error models into the calibration, prediction, and uncertainty …
structural error due to simplification and/or misrepresentation of the real system, which
raises questions regarding the suitability of conventional least squares regression‐based
(LSR) calibration. We present a new framework that explicitly describes the model structural
error statistically in an inductive, data‐driven way. We adopt a fully Bayesian approach that
integrates Gaussian process error models into the calibration, prediction, and uncertainty …
A Bayesian approach to improved calibration and prediction of groundwater models with structural error.
XTF Xu TianFang, AJ Valocchi - 2015 - cabidigitallibrary.org
Numerical groundwater flow and solute transport models are usually subject to model
structural error due to simplification and/or misrepresentation of the real system, which
raises questions regarding the suitability of conventional least squares regression-based
(LSR) calibration. We present a new framework that explicitly describes the model structural
error statistically in an inductive, data-driven way. We adopt a fully Bayesian approach that
integrates Gaussian process error models into the calibration, prediction, and uncertainty …
structural error due to simplification and/or misrepresentation of the real system, which
raises questions regarding the suitability of conventional least squares regression-based
(LSR) calibration. We present a new framework that explicitly describes the model structural
error statistically in an inductive, data-driven way. We adopt a fully Bayesian approach that
integrates Gaussian process error models into the calibration, prediction, and uncertainty …
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