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

Water Resources Research

Volumn 46, Issue 10, 2010, Pages

  Keating, Elizabeth H ^a   Doherty, John ^b,c   Vrugt, Jasper A ^a,d,e   Kang, Qinjun ^a  

^a LOS ALAMOS NATIONAL LABORATORY   (United States)

^b FLINDERS UNIVERSITY   (Australia)

^c Watermark Numerical Computing   (Australia)

^d University of California at Irvine   (United States)

^e UNIVERSITY OF AMSTERDAM   (Netherlands)

Author keywords

[No Author keywords available]

Indexed keywords

ANALYSIS ALGORITHMS; BAYESIAN APPROACHES; CPU-INTENSIVE; DIFFERENTIAL EVOLUTION; DIMENSIONALITY REDUCTION; EFFICIENT METHOD; ENVIRONMENTAL MODEL; FLOW AND TRANSPORT; GENERAL METHODOLOGIES; GLOBAL METHODS; GRADIENT-BASED SEARCH; GROUNDWATER MODELS; HIGHLY NONLINEAR; KEY CHARACTERISTICS; LOCAL MINIMUMS; MONTE CARLO; PARAMETERIZED; PREDICTIVE UNCERTAINTY; PROCESS MODEL; STRONGLY NONLINEAR; SURROGATE MODEL; UNCERTAINTY ANALYSIS TECHNIQUE; UNCERTAINTY ASSESSMENT;

AQUIFERS; BAYESIAN NETWORKS; EVOLUTIONARY ALGORITHMS; GROUNDWATER RESOURCES; HYDROGEOLOGY; INVERSE PROBLEMS; MONTE CARLO METHODS; PARAMETER ESTIMATION; PARAMETERIZATION;

UNCERTAINTY ANALYSIS;

ALGORITHM; AQUIFER; ASSESSMENT METHOD; BAYESIAN ANALYSIS; CALIBRATION; ENVIRONMENTAL MODELING; ESTIMATION METHOD; GROUNDWATER FLOW; GROUNDWATER RESOURCE; HYDROLOGICAL MODELING; MONTE CARLO ANALYSIS; NONLINEARITY; OPTIMIZATION; PARAMETERIZATION; UNCERTAINTY ANALYSIS;

EID: 77958611862     PISSN: 00431397     EISSN: None     Source Type: Journal    
DOI: 10.1029/2009WR008584     Document Type: Article

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