Maximum likelihood Bayesian averaging of spatial variability models in unsaturated fractured tuff

By Ming Ye, Shlomo P. Neuman, P. Meyer. 2003

232 citations recorded by the source corpus.

Subfields: Environmental Engineering, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/5f4ae8f9f02be005ad21f6fc0d905dd410b03393

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In Environmental Engineering

In Ocean Engineering

Citations in the corpus

An adaptive sparse‐grid high‐order stochastic collocation method for Bayesian inference in groundwater reactive transport modeling (2013, methodology): “While this study is only for quantification of parametric uncertainty, the results can be used directly for quantification of model uncertainty, because quantifying parametric uncertainty is the basis of quantifying model structure uncertainty in the popular multimodel analysis methods [Neuman, 2003”
Inverse groundwater modeling for hydraulic conductivity estimation using Bayesian model averaging and variance window (2008, methodology): “[46] Using KIC in MLBMA or minimized KIC in BMA presents the same issue in model selection and model ranking, as shown in Table 2.”
Sensitivity analysis and assessment of prior model probabilities in MLBMA with application to unsaturated fractured tuff (2005, methodology): “, 2001] for each borehole and average each of them over all six boreholes, the log score [Ye et al., 2004]”
Quantification of uncertainty in pedotransfer function‐based parameter estimation for unsaturated flow modeling (2009, methodology): “The mean parameter estimates, as observed by Ye et al. [2007a], cannot fully capture the spatial variability.”
Multimodel Simulation of Water Flow in a Field Soil Using Pedotransfer Functions (2009, background): “For example, Ye et al. (2004) suggested using the weighted results of several spatial variability models for unsaturated fractured tuff to run fl ow simulations for situations where standard information criteria provide an ambiguous ranking of the models such that it does not justify selecting one…”
Assessing the impacts of parameter uncertainty for computationally expensive groundwater models (2006, methodology): “When competing mathematical and/or conceptual models are available, maximum likelihood Bayesian model averaging (MLBMA) [Neuman, 2003; Ye et al., 2004 ] can be used to assess uncertainty in the quantities of interest.”
Combining multiobjective optimization and Bayesian model averaging to calibrate forecast ensembles of soil hydraulic models (2008, background): “…forecasting of surface temperature [Raftery et al., 2005], surface temperature and sea level pressure [Vrugt et al., 2006a], streamflow [e.g., Kuczera et al., 2006; Vrugt and Robinson, 2007b; Ajami et al., 2007], and permeability structures in groundwater hydrology [Neuman, 2003; Ye et al., 2004].”
On model selection criteria in multimodel analysis (2007, methodology): “Next, we characterize the predictive capabilities of each model in terms of its predictive log score (a measure of information lost upon eliminating from consideration some of the data for purposes of cross validation; calculation of the log score is based on equations (20)–(22) of Ye et al. [2004])”
Variance-based global sensitivity analysis for multiple scenarios and models with implementation using sparse grid collocation (2015, methodology): “…and can be estimated using various methods (Foglia et al., 2013; Lu et al., 2013; Tsai and Elshall, 2013; Ye et al. (2010b); Tsai and Li, 2008; Ye et al. (2008a); Meyer et al., 2007; Ajami et al., 2007; Poeter and Anderson, 2005; Ye et al., 2004; Winter and Nychka, 2010; Wohling and Vrugt, 2008).”
Ensemble evaluation of hydrological model hypotheses (2010, background): “Ensemble simulation studies have been undertaken in groundwater modeling [e.g., Neuman, 2003; Ye et al., 2004; Poeter and Anderson, 2005], where different model structures mean mostly different models of spatially heterogeneous parameterizations.”
Expanding the role of reactive transport models in critical zone processes (2017, methodology): “, 2014) and Maximum Likelihood Bayesian Model Averaging (BMA) framework (Neuman et al., 2012; Ye et al., 2004), can aid in evaluating parameter sensitivity across multiple categories and assist in model reduction.”
Reducing hydrologic model uncertainty in monthly streamflow predictions using multimodel combination (2012, methodology): “…or using statistical techniques such as multiple linear regression [Krishnamurti et al., 1999], Bayesian model averaging [Hoeting et al., 1999; Ye et al., 2004; Marshall et al., 2005; Vrugt et al., 2006; Vrugt and Robinson, 2007; Duan et al., 2007], or a more flexible hierarchical modeling…”
Uncertainty in hydrologic modeling: Toward an integrated data assimilation framework (2007, background): “Neuman [2002, 2003] proposed a maximum likelihood version of BMA (MLBMA) that proves to be more computationally feasible and capable of dealing with situations where reliable prior information is lacking [Ye et al., 2004, 2005].”