Optimization and uncertainty assessment of strongly nonlinear groundwater models with high parameter dimensionality

By Elizabeth H. Keating, John Doherty, Jasper A. Vrugt, Qinjun Kang. 2010

199 citations recorded by the source corpus.

Subfields: Environmental Engineering, Geochemistry and Petrology, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/8c66677f01ac652c8a86b6e5523ff96850e53bbc

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In Environmental Engineering

In Geochemistry and Petrology

In Ocean Engineering

Citations in the corpus

Review of surrogate modeling in water resources (2012, background): “Modern simulation models tend to be computationally intensive as they rigorously represent detailed scientific knowledge about the real-world systems [Keating et al., 2010; Mugunthan et al., 2005; Zhang et al., 2009].”
Markov chain Monte Carlo simulation using the DREAM software package: Theory, concepts, and MATLAB implementation (2016, background): “, 2011), hydrologeology (Keating et al., 2010; Laloy et al., 2013; Malama et al., 2013), hydrology (Vrugt et al.”
Evolutionary algorithms and other metaheuristics in water resources: Current status, research challenges and future directions (2014, methodology): “When dealing with simulation-based optimisation, an alternative is to emulate the simulation model by developing a nondynamic mapping (regression) of the complex model inputs into the outputs, namely objective functions and/or constraints (Bieker et al., 2007; Blanning, 1975; Broad et al., 2005, 201”
Efficient posterior exploration of a high‐dimensional groundwater model from two‐stage Markov chain Monte Carlo simulation and polynomial chaos expansion (2013, methodology): “…are not very well resolved [e.g., Carrera and Neuman, 1986a, 1986b; Kitanidis, 1986, 1997; Carrera et al., 2005; Cooley and Christensen, 2006; Hernandez et al., 2006; Tonkin and Doherty, 2009; Keating et al., 2010; Troldborg et al., 2010; Nowak et al., 2010; Zeng and Zhang, 2010, and many others].”
Bayesian calibration of a large‐scale geothermal reservoir model by a new adaptive delayed acceptance Metropolis Hastings algorithm (2011, methodology): “While these methods have been successfully implemented for calibrating hydrology models, in many cases they also suffer from difficulties such as inadequately exploring the parameter space [Duan et al., 1992; Bates and Campbell, 2001; Liu and Gupta, 2007; Keating et al., 2010].”
Parameter Analysis and Estimates for the MODIS Evapotranspiration Algorithm and Multiscale Verification (2019, methodology): “For parameter optimization, Bayesian‐based methods have gained considerable attention from the environmental modeling community because they provide a powerful tool from which inferences can be drawn (Clark & Gelfand, 2006; Huisman et al., 2010; Keating et al., 2010; Zhu et al., 2014).”
Global sensitivity analysis in hydrological modeling: Review of concepts, methods, theoretical framework, and applications (2015, unknown): “Model calibration or parameter optimization is challenging for complex models due to the uncertainty of a large number of parameters (Fienen et al., 2009; Foglia et al., 2009; Keating et al., 2010; Ye et al., 2014).”
Physically based modeling in catchment hydrology at 50: Survey and outlook (2015, background): “For PDE-based numerical models the main challenges to robust calibration and estimation regard computational cost, nonunique solutions (ill-posedness), and multiple possible objectives and variables [e.g., Anderman and Hill, 1999; Keating et al., 2010].”
Numerical assessment of metamodelling strategies in computationally intensive optimization (2012, background): “A single run of such models can be prohibitively long, in the order of minutes to days (Keating et al., 2010; Razavi et al., 2010; Wang and Shan, 2007; Zhang et al., 2009); while in many optimization-based engineering practices, such as design”
An adaptive sparse‐grid high‐order stochastic collocation method for Bayesian inference in groundwater reactive transport modeling (2013, methodology): “One of the most popular and robust sampling techniques is the Markov Chain Monte Carlo (MCMC) method [Marshall et al., 2005; Gamerman and Lopes, 2006; Vrugt et al., 2008, 2009; Keating et al., 2010; Liu et al., 2010].”