Assessing the impacts of parameter uncertainty for computationally expensive groundwater models

By P. Mugunthan, Christine Annette Shoemaker. 2006

84 citations recorded by the source corpus.

Subfields: Environmental Engineering, Ocean Engineering, Water Science and Technology

View Paper: https://www.semanticscholar.org/paper/7338dd4333648958b9f3d6932b00683a84e195e1

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In Environmental Engineering

In Ocean Engineering

In Water Science and Technology

Citations in the corpus

Review of surrogate modeling in water resources (2012, methodology): “Most surrogateenabled auto-calibration studies involve surrogates in conjunction with optimization algorithms; in four studies, surrogates have been used with uncertainty analysis algorithms for the purpose of model calibration (i.e., GLUE in the work of Khu and Werner [2003] and Zhang et al. [2009]”
Generalized likelihood uncertainty estimation (GLUE) using adaptive Markov Chain Monte Carlo sampling (2008, methodology): “To compensate for this drawback, the LHS method typically requires many thousands of model simulations [39,43,41,46].”
Efficient prediction uncertainty approximation in the calibration of environmental simulation models (2008, methodology): “The use of independent DDS optimization trials to search for each behavioral solution distinguishes the DDS-AU sampling methodology from the methodology proposed by Mugunthan and Shoemaker [2006] that extracts multiple behavioral solutions after a declustering technique is applied from the search hi”
Appraisal of the generalized likelihood uncertainty estimation (GLUE) method (2008, methodology): “Attractive algorithms are presented by Kavetski et al. [2002], Marshall et al. [2004], Vrugt et al. [2005], Mugunthan and Shoemaker [2006] , Tolson and Shoemaker [2008] and Blasone et al. [2008a].”
Managing computational complexity using surrogate models: a critical review (2020, methodology): “…et al. 1998), GMSRBF, MLMSRBF (Regis and Shoemaker 2007), and Gutmann’s method (Gutmann 2001), and uncertainty analysis algorithms, such as ACUARS (Mugunthan and Shoemaker 2006) and RBF-enabled MCMC (Bliznyuk et al. 2008), can help circumvent the computational budget limitations associated with…”
A Bayesian approach for inverse modeling, data assimilation, and conditional simulation of spatial random fields (2010, background): “It is a fundamental tenet of statistics that samples must be drawn at random [Mugunthan and Shoemaker, 2006] in order to prevent bias.”
Numerical assessment of metamodelling strategies in computationally intensive optimization (2012, methodology): “There is a sizeable body of literature developing and applying a variety of metamodelling strategies in various environmental and water resources related problems including surface water model and groundwater model calibration (Bliznyuk et al., 2008; Keating et al., 2010; Khu et al., 2004; Khu and W”