Estimation of Hydraulic Conductivity and Its Uncertainty from Grain-Size Data Using GLUE and Artificial Neural Networks

Mathematical Geosciences

Volumn 44, Issue 6, 2012, Pages 739-763

  Rogiers, Bart ^a,b   Mallants, Dirk ^e   Batelaan, Okke ^b,c   Gedeon, Matej ^a   Huysmans, Marijke ^b   Dassargues, Alain ^b,d  

^a BELGIAN NUCLEAR RESEARCH CENTRE   (Belgium)

^b UNIVERSITY OF LEUVEN   (Belgium)

^c VRIJE UNIVERSITEIT BRUSSEL   (Belgium)

^d UNIVERSITY OF LIÈGE   (Belgium)

^e CSIRO   (Australia)

Author keywords

Artificial neural networks; Cross validation; Data driven modelling; Early stopping; Generalised likelihood uncertainty estimation; GLUE ANN; Likelihood measures; Principal component analysis; Sedimentary aquifer

Indexed keywords

CROSS VALIDATION; EARLY STOPPING; GLUE-ANN; LIKELIHOOD MEASURES; SEDIMENTARY AQUIFERS; UNCERTAINTY ESTIMATION;

AQUIFERS; ESTIMATION; FORECASTING; GLUES; GLUING; GROUNDWATER FLOW; HYDRAULIC CONDUCTIVITY; LINEAR REGRESSION; NEURAL NETWORKS; PRINCIPAL COMPONENT ANALYSIS; REACTOR CORES; SEDIMENTOLOGY; SIZE DISTRIBUTION; STOCHASTIC MODELS;

UNCERTAINTY ANALYSIS;

AQUIFER; ARTIFICIAL NEURAL NETWORK; GRAIN SIZE; GROUNDWATER FLOW; HYDRAULIC CONDUCTIVITY; NUMERICAL MODEL; POLLUTANT TRANSPORT; PREDICTION; PRINCIPAL COMPONENT ANALYSIS; REGRESSION ANALYSIS; UNCERTAINTY ANALYSIS;

BELGIUM;

EID: 84864283555     PISSN: 18748961     EISSN: 18748953     Source Type: Journal    
DOI: 10.1007/s11004-012-9409-2     Document Type: Article

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